U.S. patent application number 14/629456 was filed with the patent office on 2015-06-18 for recommendation agent using a personality model determined from mobile device data.
The applicant listed for this patent is ARO, Inc.. Invention is credited to Kevin Francis Eustice, Andrew F. Hickl, Michael Perkowitz.
Application Number | 20150170042 14/629456 |
Document ID | / |
Family ID | 49995366 |
Filed Date | 2015-06-18 |
United States Patent
Application |
20150170042 |
Kind Code |
A1 |
Perkowitz; Michael ; et
al. |
June 18, 2015 |
RECOMMENDATION AGENT USING A PERSONALITY MODEL DETERMINED FROM
MOBILE DEVICE DATA
Abstract
A user's context history is analyzed to build a personality
model describing the user's personality and interests. The
personality model includes a plurality of metrics indicating the
user's position on a plurality of personality dimensions, such as
desire for novelty, tendency for extravagance, willingness to
travel, love of the outdoors, preference for physical activity, and
desire for solitude. A customized recommendation agent is then
built based on the personality model, which selects a
recommendation from a corpus to present to the user based on an
affinity between the user's personality and the selected
recommendation.
Inventors: |
Perkowitz; Michael;
(Seattle, WA) ; Eustice; Kevin Francis; (Seattle,
WA) ; Hickl; Andrew F.; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ARO, Inc. |
Seattle |
WA |
US |
|
|
Family ID: |
49995366 |
Appl. No.: |
14/629456 |
Filed: |
February 23, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13950117 |
Jul 24, 2013 |
9020864 |
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14629456 |
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61675733 |
Jul 25, 2012 |
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61675732 |
Jul 25, 2012 |
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Current U.S.
Class: |
706/11 ; 706/12;
706/46 |
Current CPC
Class: |
H04W 4/21 20180201; G06N
5/04 20130101; H04W 4/029 20180201; G06F 16/955 20190101; G06N
20/00 20190101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; G06N 99/00 20060101 G06N099/00 |
Claims
1. A method of creating a customized recommendation agent for a
user, the method comprising: obtaining a plurality of labelled
context slices derived from context data associated with a user,
each labelled context slice including a time, a location, and a
user context label specifying at least a place inferred from the
location; obtaining place features of places included in the
obtained plurality of labelled context slices, the obtained place
features relevant to personality traits of the user; identifying,
using the plurality of labelled context slices, one or more home
areas corresponding to one or more places at which the user has
spent a majority of time spanned by the labelled context slices;
identifying, from the places included in the plurality of labelled
context slices, one or more non-home areas corresponding to one or
more places that do not correspond to the one or more home areas;
determining a home area statistic and a non-home area statistic
from the obtained place features, the home area statistic
describing place features of the one or more home areas, the
non-home area statistic describing place features of the one or
more non-home areas; determining, by a processor, a personality
metric based on the home area statistic and the non-home area
statistic, the personality metric quantifying a personality trait
dimension of the user; and creating the customized recommendation
agent configured to provide a recommendation to the user responsive
to the personality metric indicating the user is likely to find
value in the recommendation.
2. The method of claim 1, wherein obtaining the place features
comprises determining a category that groups similar places in one
of the identified non-home areas, and wherein determining the
non-home area statistic comprises determining a frequency of visits
to the category of the one of the non-home areas.
3. The method of claim 1, wherein obtaining the place features
comprises determining distances from the identified one or more
non-home areas to a geographically nearest home area, and wherein
determining the non-home area statistics comprises determining a
non-home area statistic summarizing the determined distances from
the identified one or more non-home areas to the geographically
nearest home area.
4. The method of claim 1, wherein determining the home area
statistic comprises: determining a proportion of visits to an
identified home area relative to total visits to the places
specified by the plurality of labelled context slices.
5. The method of claim 1, wherein the personality trait dimension
includes at least one of: desire for novelty, desire for
extravagance, willingness to travel, love of the outdoors,
preference for physical activity, and desire for solitude.
6. The method of claim 1, wherein the customized recommendation
agent provides the recommendation to the user by performing steps
comprising: identifying a reason why the recommendation was
selected; and providing the reason for presentation to the user in
conjunction with the recommendation.
7. The method of claim 1, wherein the customized recommendation
agent provides the recommendation to the user by performing steps
comprising: receiving an input context associated with the user;
selecting the recommendation from a corpus of recommendations based
on the determined personality metric and the input context; and
providing the recommendation for presentation to the user.
8. The method of claim 7, wherein selecting the recommendation
comprises: determining a weight for each of a plurality of
recommendations from the corpus, each weight based on a degree of
correspondence between the personality metric and a corresponding
recommendation; and selecting the recommendation from the plurality
of recommendations responsive to the weight corresponding to the
recommendation.
9. The method of claim 8, wherein the recommendation corresponds to
a venue, wherein determining the weight comprises: adjusting the
weight corresponding to the recommendation based on a rating for
the venue provided by another user.
10. The method of claim 1, further comprising: receiving feedback
indicating how the user responded to the recommendation; and
updating the personality metric based on the feedback.
11. The method of claim 10, wherein the feedback indicates at least
one of: the user following the recommendation, the user adding the
recommendations to a plan, the user partially following the
recommendation, and the user rejecting the recommendation.
12. The method of claim 1, further comprising: providing the user
with a series of questions, each question a binary choice that
determines affinity for one of the personality trait dimensions;
and adjusting at least one personality metric of the plurality
based on the user's responses to the series of questions.
13. A non-transitory computer-readable storage medium comprising
executable computer program code, the computer program code
comprising instructions for: obtaining a plurality of labelled
context slices derived from context data associated with a user,
each labelled context slice including a time, a location, and a
user context label specifying at least a place inferred from the
location; obtaining place features of places included in the
obtained plurality of labelled context slices, the obtained place
features relevant to personality traits of the user; identifying,
using the plurality of labelled context slices, one or more home
areas corresponding to one or more places at which the user has
spent a majority of time spanned by the labelled context slices;
identifying, from the places included in the plurality of labelled
context slices, one or more non-home areas corresponding to one or
more places that do not correspond to the one or more home areas;
determining a home area statistic and a non-home area statistic
from the obtained place features, the home area statistic
describing place features of the one or more home areas, the
non-home area statistic describing place features of the one or
more non-home areas; determining a personality metric based on the
home area statistic and the non-home area statistic, the
personality metric quantifying a personality trait dimension of the
user; and creating the customized recommendation agent configured
to provide a recommendation to the user responsive to the
personality metric indicating the user is likely to find value in
the recommendation.
14. The medium of claim 13, wherein obtaining the place features
comprises determining a category that groups similar places in one
of the identified non-home areas, and wherein determining the
non-home area statistic comprises determining a frequency of visits
to the category of the one of the non-home areas.
15. The medium of claim 13, wherein obtaining the place features
comprises determining distances from the identified one or more
non-home areas to a geographically nearest home area, and wherein
determining the non-home area statistics comprises determining a
non-home area statistic summarizing the determined distances from
the identified one or more non-home areas to the geographically
nearest home area.
16. The medium of claim 13, wherein determining the home area
statistic comprises: determining a proportion of visits to an
identified home area relative to total visits to the places
specified by the plurality of labelled context slices.
17. The medium of claim 13, wherein the customized recommendation
agent is configured to provide the recommendation to the user by
performing steps comprising: receiving an input context associated
with the user; selecting the recommendation from a corpus of
recommendations based on the determined personality metric and the
input context; and providing the recommendation for presentation to
the user.
18. The medium of claim 17, wherein selecting the recommendation
comprises: determining a weight for each of a plurality of
recommendations from the corpus, each weight based on a degree of
correspondence between the personality metric and a corresponding
recommendation; and selecting the recommendation from the plurality
of recommendations responsive to the weight corresponding to the
recommendation.
19. The medium of claim 13, wherein the computer program code
further comprises instructions for: receiving feedback indicating
how the user responded to the recommendation; and updating the
personality metric based on the feedback.
20. A system for creating a customized recommendation agent for a
user, the system comprising: a processor; and a non-transitory
computer-readable storage medium comprising computer program code
executable by the processor, the computer program code comprising
instructions for: obtaining a plurality of labelled context slices
derived from context data associated with a user, each labelled
context slice including a time, a location, and a user context
label specifying at least a place inferred from the location;
obtaining place features of places included in the obtained
plurality of labelled context slices, the obtained place features
relevant to personality traits of the user; identifying, using the
plurality of labelled context slices, one or more home areas
corresponding to one or more places at which the user has spent a
majority of time spanned by the labelled context slices;
identifying, from the places included in the plurality of labelled
context slices, one or more non-home areas corresponding to one or
more places that do not correspond to the one or more home areas;
determining a home area statistic and a non-home area statistic
from the obtained place features, the home area statistic
describing place features of the one or more home areas, the
non-home area statistic describing place features of the one or
more non-home areas; determining a personality metric based on the
home area statistic and the non-home area statistic, the
personality metric quantifying a personality trait dimension of the
user; and creating the customized recommendation agent configured
to provide a recommendation to the user responsive to the
personality metric indicating the user is likely to find value in
the recommendation.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. application Ser.
No. 13/950,117, filed Jul. 24, 2013, which claims the benefit of
U.S. Provisional Application No. 61/675,733, filed Jul. 25, 2012,
and U.S. Provisional Application No. 61/675,732, filed Jul. 25,
2012, each of which is incorporated herein by reference in its
entirety.
BACKGROUND
[0002] 1. Technical Field
[0003] The described embodiments pertain to making recommendations
to a user based on location data and other data collected from
mobile devices.
[0004] 2. Description of Related Art
[0005] A variety of personalized recommendation systems currently
exist. These systems typically record user events (such as
purchases of particular products) and/or preferences (such as
ratings of products and media) to generate a model for the user.
This model is then used to recommend "similar" items to the user.
However, there are a number of obstacles to building useful
applications on top of the kinds of user models currently
available.
[0006] First, existing models are not very effective until the
system has observed a great deal of user behavior or preferences.
Second, the interests and tastes of a typical user are more nuanced
than the general genres and/or category preferences that existing
models generate. Third, existing models recommend things similar to
what the user has seen before, whereas many users are interested in
being presented with new options to explore.
[0007] As a result, many recommendation systems are limited in
their usefulness to particular scenarios. For example, they may
help a pizza fan find more pizza restaurants, a science fiction fan
find more science fiction novels, or a lover of romantic comedies
starring Jennifer Aniston find more romantic comedies starring
Jennifer Aniston. However, they cannot help the pizza fan find a
great burger joint, the science fiction fan find her friend's
favorite science fiction novel, or the Jennifer Aniston fan find a
bar that makes a great Martini.
SUMMARY
[0008] Embodiments of the invention include a method, a
non-transitory computer readable storage medium and a system for
creating a customized recommendation agent that makes
recommendations to the user based on a context-based routine model
for the user. A context is a (possibly partial) specification of
what a user was doing in the dimensions of time, place, and
activity. Each of these dimensions may be defined specifically
(e.g., location defined by latitude 47.60621, longitude
-122.332071) or very generally (e.g., the location "Seattle, WA"),
or entirely unspecified (e.g., omitted or a default value). They
may also be ascribed varying degrees of semantic meaning (e.g.,
"Seattle" contains more semantic information than "47.60621,
-122.332071"). A context can represent a stay in a certain location
or travel from one place to another. Contexts may have
probabilities associated with them. In some cases, contexts may be
inferred from evidence rather than known with certainty. Thus,
contexts can vary in their specificity, their semantic content, and
their likelihood.
[0009] A user's context history is analyzed to build a personality
model describing the user's personality and interests. The
personality model includes a plurality of metrics indicating the
user's position on a plurality of personality dimensions, such as
desire for novelty, tendency for extravagance, willingness to
travel, love of the outdoors, preference for physical activity, and
desire for solitude. A customized recommendation agent is then
built based on the personality model, which selects a
recommendation from a corpus to present to the user based on an
affinity between the user's personality and the selected
recommendation.
[0010] Embodiments of the computer-readable storage medium store
computer-executable instructions for performing the steps described
above. Embodiments of the system further comprise a processor for
executing such computer-executable instructions.
[0011] The features and advantages described in the specification
are not all inclusive and, in particular, many additional features
and advantages will be apparent to one of ordinary skill in the art
in view of the drawings, specification, and claims. Moreover, it
should be noted that the language used in the specification has
been principally selected for readability and instructional
purposes, and may not have been selected to delineate or
circumscribe the inventive subject matter.
BRIEF DESCRIPTION OF DRAWINGS
[0012] FIG. 1 is a block diagram illustrating the system
environment for creating and using recommendation agents in
accordance with an embodiment of the invention.
[0013] FIG. 2 is a diagram illustrating the relationship of sensor
data, slices, and contexts in accordance with an embodiment of the
invention.
[0014] FIG. 3 is a flow chart illustrating a process of creating
and updating a user's contextual history in accordance with an
embodiment of the invention.
[0015] FIG. 4 is a flow chart illustrating a process of slicing in
accordance with an embodiment of the invention.
[0016] FIG. 5 is a flow chart illustrating a process of labeling in
accordance with an embodiment of the invention.
[0017] FIG. 6 is a block diagram illustrating one embodiment of the
recommendation agent module shown in FIG. 1.
[0018] FIG. 7 is a flow chart illustrating a method of making a
recommendation based on a user's routine in accordance with an
embodiment of the invention.
[0019] FIG. 8 is a flow chart illustrating a method of making a
recommendation based on personality metrics for a user in
accordance with an embodiment of the invention.
[0020] The figures depict embodiments of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following description that alternative
embodiments of the structures and methods illustrated herein may be
employed without departing from the principles of the invention
described herein.
DETAILED DESCRIPTION
[0021] Embodiments of the invention make recommendations to a user
based on the user's routine and/or metrics describing the user's
personality, as determined from context slices generated from
observed data. A context is a (possibly partial) specification of
what a user was doing in the dimensions of time, place, and
activity. Contexts can vary in their specificity, their semantic
content, and their likelihood. Raw context data can be collected
from a variety of observation sources with various error
characteristics. Slicing refines the chaotic collection of contexts
produced by data collection into consistent segments, called
slices, each with a corresponding set of contexts. Accordingly, the
slices and corresponding context data are then available for
further processing, such as determining features of the user's
routine and/or metrics describing the user's personality.
System Overview
[0022] FIG. 1 is a block diagram illustrating the system
environment 100 for creating and using recommendation agents, in
accordance with an embodiment of the invention. The system
environment 100 includes one or more raw context collectors 110, a
context refiner module 120, contextual history storage 130, and a
recommendation agent module 140. In one embodiment, the entities of
the system environment 100 may be contained on a single processing
node, such as a user's mobile device, and in another embodiment,
the entities of the system environment may be divided between the
user's mobile device and a centralized processing node to decrease
the processing requirements of the mobile device. An example
distributed embodiment is one where the raw context collectors 111
are located on the user's mobile device and data is sent to a
central device for context refinement, storage, and agent
building.
[0023] The raw context collectors 110 collect raw context data from
observation sources, sensors, monitors, third party sources, and
the like. A raw context represents a single observation and so is
generally very specific, often carries little semantic information
on its own, and is of high probability. Naturally, different
observation sources may have greater degrees of noise and
uncertainty or may inherently include semantic information. In the
example illustrated in FIG. 1, the raw context collectors 110
include a location module 111, an activity module 112, and a social
module 113, but different and/or other context collectors 110 may
be included in other embodiments. For example, in various
embodiments, the context collectors include sensors for measuring
device orientation (e.g., a compass), magnetic fields, user heart
rate, and user stress level, as well as modules for audio and
visual sampling of the user's environment.
[0024] Examples of location module 111 include a GPS receiver, and
a Wi-Fi receiver that enable the location module 111 to determine
an absolute or relative location of the user's mobile device,
within a margin of error. Examples of the activity module 112
include, a monitor of key presses that determines when a user is
typing or otherwise interacting with the user's mobile device, and
an accelerometer that measures the acceleration forces on the
mobile device to determine movement and movement patterns of the
mobile device. Examples of social module 113 include a FACEBOOK
friend graph, PINTEREST pins, FOURSQUARE check ins, and other
social media data that identify a user's social acquaintances,
activities, and locations.
[0025] The context refiner module 120 receives the raw context data
from the raw context collectors 110. The context refiner module 120
groups the context data by combining the observations into slices
in order to create a more coherent representation of the user's
context during the time range represented by the slice. Each slice
includes one or more contexts that apply to the user for the time
range corresponding to the slice. The context refiner module 120
may also attach semantic content to a slice or sequence of slices
to provide additional contextual information, such as an activity
that the slice or slices represent, general locations like a city
or neighborhood, specific business locations, the time of day, or
the day of the week. In some embodiments, the context refiner
module 120 includes a plurality of context refiner sub-modules (not
shown); one for each type of context data received from the raw
context collectors 110. Each context refiner sub-module groups
context data by combining the observations from the corresponding
raw context collector 110 into slices in order to create a more
coherent representation of the user's context indicated by the
specific type of context data the sub-module is operating on. In
one such embodiment, the context refiner module 120 includes an
additional refiner sub-module (not shown) that analyzes the
multiple streams of contexts generated by the other context refiner
sub-modules to detect overlapping slices and generate combined
slices containing context information from the corresponding
overlapping slices.
[0026] The contextual history storage 130 receives the slices
formed by the context refiner module 120 from a user's raw
contextual data and stores them in association with an identifier
of the user. Thus, the contextual history storage 130 contains a
collection of slices that collectively describe the user's
contextual history. This collection may consist of multiple
parallel streams of contextual information as well as a single
master stream composing all these contexts together. The
recommendation agent module 140 can then access the stored slices
in order to build recommendation agents (and make recommendations)
that are customized for the user. The processes of creating slices
and building customized recommendation agents will be described in
detail in the sections below.
[0027] FIG. 2 is a diagram illustrating the relationship of sensor
data, slices, and contexts, in accordance with an embodiment of the
invention. FIG. 2 illustrates observations, which are the
collections of sensor data from a mobile device. In this example,
the observations include Global Positioning System readings,
Cellular triangulation signals, and emails sent from the device.
The points along the time axis at which these observations are
collected are marked. As a result of these observations, the user's
time can be divided into a sequence of slices. In this example,
each slice has a type and a start and end time. In this example,
the type is either "stay" or "travel" and the start and end times
establish the limits or boundaries of the slice. For "stay" type
slices, the slice also has a centroid location, and for "travel"
type slices, the slice also has a speed of the travel. In this
example, based on the division of observations into time slices, a
variety of metadata representing contexts have been attached to the
slices. The metadata may describe the dimensions of time, place,
and activity of the user at various levels of generality. For
example, the first slice is associated with a place dimension of
the context at various levels of generality/specificity: at a city
level, a street address level, and a venue level.
Contextual History Creation Process
[0028] Embodiments of the invention divide the process of the
creation and updating of a user's contextual history into three
phases: data collection, slicing, and labeling. These phases are
illustrated in the flow chart of FIG. 3, and described in detail in
this section.
Data Collection 301
[0029] Data collection 301 involves accessing the various sources
of information and observations of user behavior, optionally
transporting their data to servers for analysis and storage (to
offload CPU load and reduce battery usage), and translating the
data into a collection of raw contexts for additional analysis.
These observations may come from a variety of sources, including
but not limited to the following: [0030] Location technologies
(e.g., GPS, cell tower triangulation, Wi-Fi location), typically
embedded in mobile devices like smartphones. The location
technologies may be included, for example, in the location module
111. [0031] Activity data such as accelerometer data from mobile
devices and device interaction notices (e.g. taps, key presses,
power on). The activity data may be collected, for example, by the
activity module 112. [0032] Ambient data from the user's
environment (e.g., sound, temperature, or light intensity). [0033]
Biometric data such as the user's skin temperature, galvanic skin
response, heat flux, and heart rate. This data may be used to
calculate caloric burn, stress level, sleep quality, and other
indicators of the user's physical state. [0034] Social data from
any networks or applications the user may use, such as FACEBOOK,
TWITTER, FOURSQUARE, FLICKR, or PINTEREST, as well as personal
communication applications like email and text messaging. The
social data may be collected, for example, by the social module
113. In one embodiment, the social data is made available to the
social module 113 through application programming interfaces
(APIs). [0035] Schedule data, such as from the user's calendar
application. [0036] Explicit annotation created by the user to
inform the system of a location (e.g., a "check in" at a baseball
stadium) or activity (e.g., marking the time boundaries of a jog to
track fitness goals). Data collection 301 may be run as a constant
on-going process, with different techniques appropriate to
different sources. Alternatively, data collection 301 may be run
periodically at an interval appropriate for the application.
Slicing 302
[0037] One challenge with data collection 301 is that multiple
sources of observation data may result in a collection of contexts
that contain conflicting information. For example, an observation
from the user's calendar may place him at a meeting downtown, while
GPS observations may show him to be at the beach. Resolving these
inconsistencies is key to the interpretation of the data. Slicing
302 is the process of refining the chaotic, multi-threaded
collection of raw contexts produced by data collection into
consistent segments, called slices, each comprising a set of one or
more contexts. For example, in the case of location data such as
that from GPS sensors, these slices generally represent either a
stay at one place or a process of travel from one place to another.
In one embodiment, place information may be refined, in that each
stay context defines an area that includes most of the individual
points observed during that time. Travel contexts will generally
have a start and end point, with some definition of the route
between them (e.g., waypoints). In another embodiment, no
additional semantic meaning or activity information is added during
slicing 302. Other types of data can be used to produce other types
of slices, such as slices representing a period of consistent
activity. For example, an activity like "shopping" may be
represented by a longer-duration slice that overlaps with multiple
location slices representing stays at different retail businesses
as well as travel between those businesses. Conversely, an activity
like "sleeping" may span only part of a stay at home. As another
example, a biometric slice such as "high caloric burn" may cover
part of a visit to the park as well as time at the gym.
[0038] Embodiments of the invention divide the process of slicing
302 into three phases: preprocessing 410, segmentation 420, and
reconciliation 430. Each of these phases is described in detail in
this section, with reference to the flow chart illustrated in FIG.
4. The steps of FIG. 4 are illustrated from the perspective of the
context refiner module 120 performing the method. However, some or
all of the steps may be performed by other entities and/or
components. In addition, some embodiments may perform the steps in
parallel, perform the steps in different orders, or perform
different steps.
[0039] Preprocessing 410
[0040] Since raw context data input into the slicing 302 process
come from a variety of sources with varying degrees of inaccuracy,
the raw context data are systematically groomed into a suitable
form for later steps to minimize the amount of error in the output.
In one embodiment, preprocessing 410 involves a combination of
filtering 412, smoothing 414, and interpolation 416.
[0041] Filtering 412. Filters on raw context data eliminate from
consideration raw context data that are deemed more inaccurate than
some desirable threshold. The value of the filter threshold can be
sensor-specific, due to different sensor error characteristics. For
example, a GPS device's data uncertainty is calculated by physical
factors related to the timing of signals received from the device's
acquired satellites, so it can report a reliable estimate of sensor
inaccuracy. In contrast, reported uncertainty is less reliable with
location technology based on cell tower or Wi-Fi triangulation,
which lack the measurement precision necessary to account for
fluctuations in wireless signal strength, therefore the threshold
for filtering 412 those contexts may be higher. When using any
location technology, the amount of filtering 412 will depend on its
expected error characteristics, and the error characteristics are
expected to vary between sources of data. Optionally, default
threshold values for filters may be set system-wide, set per sensor
type, or based on user preferences. In addition to filtering 412 by
location technology, physically unlikely readings (e.g., traveling
at higher speeds than possible) may also be filtered.
[0042] Smoothing 414. It is also helpful to later slicing phases
for context grooming to smooth sequences of contexts from the same
sensor when each individual context is noisy. Noise characteristics
are hardware dependent, so the smoothing 414 of each sensor should
be parameterized to limit the noise expected from that sensor. For
example, a certain accelerometer may generate noisy contexts at a
high sampling rate, characterized by large magnitude swings in all
axes. One way to smooth such data is to compute an average of the
magnitude values over a time window and then output the smoothed
magnitude values at a less frequent sampling rate. Smoothing 414 is
also used when different sensors conflict. For example, if there is
minimal change in values across a series of accelerometer readings,
it indicates that a device was immobile, which could contradict a
series of location readings that would otherwise suggest the device
was wandering due to inaccurate location technology. In general,
the degree of smoothing 414 will depend on the variability in data
noise from each particular location technology.
[0043] Interpolation 416. Uneven sampling rates can also be due to
power conservation, where a device chooses to go into a low-power,
low sampling rate state, or is forced to by a governing operating
system such as a mobile phone OS. It is common for sensors to be
configured to increase sampling when the environment is changing,
and to decrease it when the environment (from the sensor's
perspective) is static. As slicing 302 occurs over a finite window
of time, a decreased sampling rate could lead to relevant context
data falling outside the window. Therefore, it is desirable in some
cases to interpolate less frequent context data to ensure that the
later phases of slicing 302 have sufficient data to analyze.
Interpolation 416 generates virtual context data between sensed
context data. For example, when there is a gap between two location
contexts, a number of interpolated context data points may be
generated that correspond to locations between the two endpoints.
Interpolation 416 runs into the risk of adding contexts that should
not exist. For example, if a sensor is not functional and therefore
not reporting, a gap in contexts should not be interpolated. To
prevent invalid interpolation 416, sensor data payload may include
an indication that there has been an interruption in contexts since
the last time a sensor generated context. This may be the default
behavior whenever a sensor is (re)started for data collection by
the controlling data collection process. In addition, if there is
an exceptionally long gap between context data from sensors, it may
indicate an interruption even if the sensors fail to set the flag
and would be treated as such.
[0044] Segmentation 420
[0045] Segmentation 420 involves determining distinct, contiguous
series of slices from the groomed sensor data representing
different activities. For example, the simple day of a user who is
an office worker could be segmented into a stay slice located in
the morning at her domicile, then a commute to work travel slice, a
stay slice at an office, then a commute back home travel slice,
followed by a stay slice in the evening at her domicile.
[0046] There are a variety of algorithms to segment the input raw
context data into stays, travels, and gaps. For example, k-means
clustering can be applied to find clusters of contexts (by
location, or a distance function combining location and time). Stay
slices can be distinguished from travel slices by the dispersion of
location context and/or velocity data. Because k-means has
fundamental limitations, other more sophisticated clustering
algorithms can be used additionally or alternatively to extract
slices.
[0047] Besides clustering, segmentation 420 can also be performed
by applying time-series analysis algorithms, using the variance of
a sliding window of input contexts to detect inflection points in
the distribution. When the variation across a subsequence of input
context data differs from a subsequence before it, the algorithm
divides the two subsequences into slices that can then be
classified as a stay or travel. For example, a stay is
distinguishable from a travel by the low amount of variance in each
individual input context in the stay sequence to its centroid, the
geographic average location.
[0048] Because there are a variety of algorithms that can be
applied to segmentation 420, each with different features and
limitations, it is also possible to combine their resulting outputs
with a meta-segmenter. This meta-segmenter can pick and choose
slices output with the highest associated probability among all
constituent segmentation algorithms.
[0049] Segmentation 420 can also be followed by filter and merge
steps that smooth the output slices. Filters can remove short
slices with more uncertainty associated with the contexts included
therein, e.g., those with few actual sensor observations, and merge
adjacent segments that are likely to be the same activity. The
thresholds on minimum required observation uncertainty or distance
from adjacent segments for filtering and merging can be
parameterized to control the false positive rate (groups of raw
context data that should not have been segmented) compared to the
false negative rate (groups of raw context data that should have
been segmented but were not).
[0050] Reconciliation 430
[0051] In one embodiment, the final phase of slicing 302 deals with
resolving newly generated slices with existing contexts generated
from a previous slicing 302 run. While this reconciliation 430 is
optional--if it were computationally feasible to run slicing 302 on
an entire raw context set, the brand new contexts could simply
replace the older ones--in some cases reconciliation 430 provides
desirable qualities for the purpose of presentation. For example,
it is desirable not to change contexts and slices in a user's
history that have been previously displayed to the user, unless new
data is in significant conflict, because the instability in data
shown to the user would appear inconsistent. Instability is even
less desirable in cases when the user has performed some operation
on a previous context or slice, such as manually labeling or
otherwise attaching metadata to it, that the subsequent slicing 302
run would overwrite. As such, there are rules governing when new
slices and contexts can replace preexisting data in a user's
history.
[0052] One way to limit the scope of changes between new and
preexisting slices is to specify a time window within which
preexisting data may be changed or replaced. Any data outside the
window (i.e., older than a certain age), would be left unchanged in
later slicing 302 runs. Contexts from newer slices are then
integrated into the eligible preexisting slices by comparing type
(stay or travel) and time spans. If a new slice is of the same type
and begins and ends at approximately the same time as an existing
slice, it could retain the same metadata of the existing slice,
including any identifier labels (ids) and contexts. When a new
slice and old slice overlap in time but conflict in type, the
process can prefer the new slice except when there has been manual
intervention, for example when a user has already interacted with
the existing slice or confirmed it in some way using a user
interface. Finally, the last slice is most likely to have changed
due to new data, and could have its ending time extended if it
aligns with a new slice starting at a time near its own start time,
or completely replaced if the type changed (if a previously
presumed stay were actually the beginning of a travel slice, for
instance).
Labeling 303
[0053] Labeling 303 is the process of adding more specific and
semantically meaningful data to the slices produced by slicing 302.
In one embodiment, some or all of these labels are themselves
contexts associated with the slices. In particular, labeling 303
adds geography (such as a slice's city or neighborhood), venue
(public places, businesses, or personally significant places like
"home"), and activity (such as "working", "eating", or "going to
see a movie"). Note that the process of labeling 303 may suggest a
revision in slicing 302, such as when the labeling 303 process
determines that a user was eating and then seeing a movie at the
theater next door, while the slicing 302 phase represented both
activities as a single slice, prompting the single slice to be
split into two successive slices, taking place at distinct
venues.
[0054] A slice can be labeled using identifiers from predefined
data sets, such as public venue records, or automatically
generated, for example using a reverse geocoding system that
converts latitude and longitude coordinates into an approximate
street address. The labeling 303 process uses these data sources to
apply probable labels to each slice. Some labels are exclusive
while others may coexist alongside one another. Example data
sources for the labeling 303 process include: [0055] Public venue
database--a set of geographically annotated public venue names,
such as businesses, public spaces, or landmarks. The venue data
should be able to be queried geographically (e.g., to find likely
venues within some specified distance of the slice's observed
location observations), therefore it should have a location
represented either as a single point (latitude, longitude,
altitude) or as a set of points that defines or approximates its
shape. The venue may also contain a unique identifier, which is
useful, for example, to use to associate the venue with manually
entered observations from the user. In addition to location and
name, the data entry for the venue may contain other metadata such
as address, business hours, categories describing the type of
venue, and reviews useful to present back to the user. Because the
set of public venues changes over time, this database may be
configured to receive updates whenever available. [0056]
User-specified database of places--a set of manually or
automatically generated locations considered private to the user,
identified by location and optionally by name and other metadata
the user chooses to attach. The purpose of this database is to
provide labels for slices that cannot be associated with public
venues due to gaps in coverage. For example, many homes are not
public venues and therefore would not be found in any public venue
database, so a user may need to manually label his/her home. Labels
such as "home" and "work" can also be automatically inferred.
[0057] A set of additional labels associated with certain venue
metadata such as a venue category. These labels could include
descriptions of activities commonly applicable to the venue
category (e.g., "jogging" at public parks or "dining out" at
restaurants). These labels may be either predefined or
automatically extracted, e.g., by analyzing the texts of some
corpora such as online reviews. As with venue or place, the user
can manually apply an activity label to a slice, or the labeling
303 process can infer it based on a model of likelihood given the
input context. [0058] Public and user-specific calendar
data--listings of public events and private appointments that can
then be applied to matching, consistent slices. [0059] A database
to store user corrections to automatically applied labels that were
made by the system in error. This database has multiple uses.
First, in the case of continuous slicing 302 and labeling 303, the
correct label can be used during reconciliation 430 to prevent
incorrect labels from being reapplied. Second, the presence of the
correction indicates with high confidence what the correct
description for the slice is, and can influence future automated
labeling 303 decisions for similar slices. The user corrections may
be stored, for example, in contextual history storage 130 or
similar data store accessible by the context refiner module
120.
[0060] Conceptually, it is possible to view the labeling 303
process as a collection of subprocesses responsible for outputting
one type of label at a time. Labels of different types can then be
run simultaneously on slices, or chained when one type of label is
triggered by the presence of another (i.e., activities that are
category- or venue-specific are only considered when a preceding
labeling 303 subprocess applies a corresponding category or venue
label, respectively, to the slice). In general, the labeling 303
process can be broken into three phases: label candidate search
510, label candidate ranking 520, and label application 530, as
illustrated in FIG. 5.
[0061] Label Candidate Search 510
[0062] In the label candidate search 510 phase, the label sources
are first queried to discover possible labels based on the slice's
existing contexts. The following provides examples of how various
label types can be searched.
[0063] Venues and places are found based on the slice's location,
which by definition is a consistent estimate of a user's location
over a period of time. However, there is a degree of uncertainty
when using the associated slice location. Essentially, raw sensors
providing locations are imprecise. The label candidate search 510
phase does not rely on the exact location represented by the slice,
but instead expands the search within some radius calculated as an
estimate of the uncertainty. For example, if a slice's location was
calculated using Wi-Fi triangulation, the triangulation error is
often in the tens to low hundreds of meters, so the search process
may query for venues and places centered at the slice location
within two hundred meters.
[0064] Events and appointments can be found based on the slice's
location and time boundaries. An event at a venue would be matched
against the overlapping time boundaries between the event and the
slice. Appointments are also matched against location and time.
Because event and appointment time boundaries are imprecise, and
slice time boundaries may be imperfect, the slice's time boundaries
do not need to exactly match those of an event or appointment.
Similarly, the slice location does not need to be an exact match
either. The label candidate search 510 finds possible events and
appointments within the likely uncertainty radius of the slice
location.
[0065] Several methods may also be used to find candidate
activities. For example, based on the category and/or venue labels
already applied to the slice, the label candidate search 510
process can bring up associated activity labels. As another
example, the slice context can be compared to similar slices in the
past if the user had previously labeled activities manually. For
example, if a user previously labeled an activity at the same venue
or a venue in the same category as the slice that has not yet been
labeled with an activity, that activity would be considered as a
candidate for labeling the slice.
[0066] Label Candidate Ranking 520
[0067] Once a set of label candidates of a given type are found,
the likelihood of each one given the contexts already associated
with the slice is evaluated. In one embodiment, the likelihood of
each label is computed and the labels are ranked. There may also be
a threshold for likelihoods, such that if no label is deemed likely
enough, none is applied to the slice at all--this avoids the case
of having a label (e.g., an incomplete label) applied
inappropriately. In one implementation, slices are constrained to
only having one label of some types (e.g., venue labels), and the
top-ranked label meeting the minimum likelihood threshold is
applied to the slice. For other label types, multiple labels can be
valid for a single slice, and all labels meeting the minimum
threshold of likeliness are applied.
[0068] Likelihoods are calculated by scoring a candidate label
given the contexts already associated with a slice. A model is
defined to be an algorithm for computing likelihoods given slice
context. Models treat the various aspects of a slice context as
features. Some example features include: [0069] Slice
location--while the label candidate search 510 also uses location
to discover the set of eligible labels to apply, a ranking model
can further determine the likelihood of a label given its distance
to the slice location, or relative likelihood between several
candidates (e.g., a venue that is significantly farther from the
slice location would to considered less likely, but two venues that
differ only a small amount in distance from the slice location may
be considered equally likely given the location context, all else
being equal). [0070] The particular user whose slice is being
labeled--since users have individual differences, a model may use
specific algorithms tailored to each to achieve the best labeling
accuracy. One example of how an individual user could affect label
candidate ranking 520 is for the ranking process to use the user's
accumulated history of slices and labels, some of which the user
may have explicitly confirmed to be accurate. Labels that occurred
more often in the user's history may be considered more likely when
labeling new contexts. [0071] The beginning and end stay
times--since different labels are more likely to occur at different
times (e.g., restaurants at meal times, rock concerts in the
evenings), and events, appointments and activities last for
different lengths of time (e.g., movies between 1.5-3 hours), the
likelihood of a particular label can depend on the corresponding
day and time range.
[0072] Besides the context provided by the slice, models can use
other sources of information to inform the likelihood estimate.
Some example information sources include: [0073] Venue hours of
operation--can be used to reduce the likelihood that a venue be
applied to a slice when the venue is known to be closed during some
significant portion of the slice's time boundaries. [0074] Venue
popularity--e.g., relative popularity over all time compared to
other venues, or historic popularity at the time of day, day of
week, etc, which can indicate the likelihood that the label is
applicable given the slice's time boundaries. If the duration of
the slice is known, it can also be compared to the distribution of
stay durations at the venue to determine whether the length of time
spent in one place is consistent with other visits to the candidate
venue. [0075] Category popularity--can be used when data is scarce
about specific venues in the candidate list. This can also be
relative to time of day, day of week, etc., and can also include
typical stay durations so that the slice's time boundaries factor
into the likelihood calculation. [0076] Routine--considering how
often in the past the user has similar slices with the candidate
label, can determine whether a certain label is more likely (if
there are many such instances) or less likely (if there are few or
no instances). Routine is not limited to only considering a
specific user's historical patterns. Users can be clustered into
cohort groups, or even aggregated into a global routine model,
depending on data scarcity due to limited interactions with the
system or the area where the slice occurs. Methods for determining
a user's routine are discussed in greater detail below, with
reference to FIG. 6. [0077] Social interest--some users are more
likely to visit a venue if their friends recommended it, if they
have been there before, or were labeled as being there during an
overlapping time period by the labeling 303 process. Some of this
information is available through existing social network APIs, for
example recommendations may be based off of a friend "liking" the
venue on FACEBOOK. Information about a user's friends visits to a
venue can also come from a friend "checking in" or retrieved from
the contextual history storage 130 (in embodiments where the
contextual history storage is centralized).
[0078] Label Application 530
[0079] One or more models can be run to process a slice's
context(s) into labels, which are applied in label application 530.
Conceptually, multiple models can be represented by a single
meta-model that runs the appropriate features through its composing
models. Once the meta-model outputs probabilities, labels deemed
sufficiently likely are applied to the slice. In one embodiment,
labels that are not deemed to be sufficiently likely can still be
surfaced as options to the user should he/she wish to alter the
label set by adding new labels, with the label candidate ranking
520 intact. In such cases, it is not necessary for the same
meta-model to produce the label candidate ranking 520--different
meta-models can rank labels differently to produce whatever is
specified by the system design of a particular embodiment.
[0080] In one embodiment, automatic label application 530 does not
require that every label is ranked solely by likelihood. For
example, when users interact with the label candidates (e.g., to
manually apply labels), it can be desirable to present candidate
labels in different orders to make finding desired labels easier.
For example, an alphabetical order or a hybrid order that
incorporates both likelihoods and lexicographical positions can be
used. Once labels are applied to slices, the additional contextual
data is presentable to the user and available for further
processing in a variety of forms.
Building Recommendation Agents
[0081] A user's history can be used to build a recommendation agent
that is customized for the user. Additional data about the user
and/or the items that are considered for recommendation can also be
used in building the recommendation agent. In various embodiments,
the recommendation agent module 140 models the user's routine
and/or personality in order to build a customized recommendation
agent. In some embodiments, the recommendation agent module 140
also updates the customized recommendation agent based on feedback
provided by the user after the agent has begun to be used.
[0082] FIG. 6 is a block diagram illustrating one embodiment of a
recommendation agent module 140. The illustrated recommendation
agent module 140 includes a routine module 610, a personality
module 620, an agent compiler module 630, a feedback module 640,
recommendation agent storage 650, and a recommendation corpus 660.
In other embodiments, the recommendation agent module 140 contains
different and/or additional elements. In addition, the functions
may be distributed among the elements in a different manner than
described herein.
[0083] The routine module 610 and personality module 620 generate
models of a user's routine and personality respectively, based on
the corresponding user history stored in contextual history storage
130. The agent compiler module 630 builds a customized
recommendation agent for the user based on the routine model, the
personality model, and/or additional information about the user
that is available to the recommendation agent module 140. The
customized recommendation agent is stored in the recommendation
agent storage 650. The feedback module 640 gathers feedback during
use of the customized recommendation agent and updates the agent
based on the gathered feedback. The recommendation corpus 660
comprises a plurality of possible recommendations and corresponding
metadata (e.g., a type of recommendation, times when the
recommendation is appropriate, types of personality that are likely
to respond positively to the recommendation, and the like).
[0084] The recommendations in the recommendation corpus 660 can be
compiled from various existing sources and/or generated by the
recommendation agent module 140. For example, FOURSQUARE provides a
publicly accessible database of venues and events that may be
included as part of the recommendation corpus 660. As another
example, the recommendation agent module 140 may maintain a list of
activities (e.g., going for a walk, eating ice-cream, etc.) and add
activities to the list if a user tags a slice with a previously
unknown activity. In some embodiments, the same data sources that
are used for labeling (as described above with reference to FIG. 3)
are used to populate the recommendation corpus 660. In one
embodiment, recommendations are compiled in an ongoing way as new
recommendations become available (e.g., new restaurants open,
events are scheduled, books are published, etc.) and added to the
recommendation corpus 660 for later delivery to the user or group
of similar users. In another embodiment, just-in-time
recommendations are generated in real-time for a user based on the
user's current context. For instance, when the user is in a new
area of the city, the recommendation agent module 140 searches
existing databases for possible recommendations in the area and the
user's personality and routine data are used to determine
appropriate recommendations (e.g., a nearby and highly rated
restaurant for dinner).
Modeling User Routine
[0085] The routine module 610 takes a user's history comprising
slices as input and generates a model describing the user's
routine. The routine model describes correlations between contexts
for the user that can be used to generate recommendations based on
the user's current contexts. For example, if a user regularly
leaves work to purchase lunch at midday, the routine model could
indicate a correlation between the contexts "midday" and "eating."
Thus, a recommendation system using the routine model might
recommend restaurants in the user's vicinity at around midday, even
if the user is not at work that day.
[0086] The routine module 610 processes the slices in the user's
history to create a collection of transitions. A transition is
defined as a change from one context to another. A transition has a
source (the starting context) and a destination (the following
context). Like contexts, transitions can have varying degrees of
specificity, semantic content, and likelihood. For example, a
user's night out could be described by any (or all) of the
following transitions: dinner followed by a movie, Joe's Pizza
followed by Regal Theaters, the Capitol Hill neighborhood followed
by the University neighborhood. The routine module 610 can
therefore record many possible transitions (with associated
probabilities) to account for a segment of observed behavior. Thus,
the series of contexts included in the slices of a user's history
also define a collection of transitions.
[0087] Embodiments of the invention use the following process to
build a routine model for a user based on that user's sequence of
historical slices. [0088] Split the slice sequence into
subsequences whenever adjacent slices are too far apart in time
(e.g., greater than three hours). [0089] Filter out any slice not
meeting certain quality criteria. For example, by filtering out
stays of duration less than five minutes, stays supported by too
few observations, and/or stays with low probability. [0090] Iterate
through the slices. Create transition pairs between contexts for
the current slice and contexts from a previous slice within some
history window (a larger window allows a wider range of data, but
can be much more expensive to compute as many more transition pairs
become possible). The context from the previous slice is the source
of the transition, and the context from the current slice is the
destination. [0091] Keep count of how often each context and each
transition pair occurs over time. Using these counts, compute the
probability of each destination given each source, i.e., the
probability of various transitions. [0092] Create and store
transition rules for the user, each transition rule comprising a
source context, a destination context, and a corresponding
probability that the user will make a transition from the source to
the destination.
[0093] The result of this process is an incrementally-updated
routine model for the user consisting of a collection of transition
rules, each with a source context, a destination context, and a
probability. For example, imagine a segment of a user's history
that involves three slices: a stay at a coffee shop in Bellevue,
travel from Bellevue to Seattle, and a stay at the user's workplace
in Seattle. The first slice might include contexts like
"location=Bellevue", "venue=Joe's Cafe", "type=coffee shop", and
"time=morning". The second might include "location=Bellevue to
Seattle" and "activity=driving". The third might include
"activity=working", "location=Seattle", and "location=456 Spring
St." The routine module 610 can generate a number of transitions
from this data and add them to the user's routine model. Examples
include a transition from "time=morning" to "activity=working", a
transition from "type=coffee shop" to "activity=driving", a
transition from "location=Bellevue" to "location=Seattle", and a
transition from "location=Bellevue" to "location=456 Spring St."
Note that, because the system is less certain that the third slice
was at 456 Spring St. than that it was in Seattle (because Seattle
is larger and easier to identify than a specific address), the "456
Spring St." transition would have lower probability than the
"Seattle" one.
[0094] Furthermore, the location module 111 might have been
uncertain whether the user was at Joe's Cafe or Barbara's Bagels
next door (e.g., due to the noise in position data), assigning a
higher probability to Joe's. In one embodiment, transitions for
both Joe's and Barbara's are generated and added to the routine
model, with the corresponding probability for Joe's being higher
than that for Barbara's. In other embodiments, the routine module
410 considers only the most likely interpretation of the data, only
transitions that exceed a threshold probability. In further
embodiments, contexts are filtered according to combinations of
these approaches.
[0095] For simplicity, the source of a transition rule has been
described above as a single context. However, transitions rules can
have sets of contexts as sources, allowing non-Markovian transition
rules. For example, a rule could encode that when a user goes to
the Capitol Hill neighborhood followed by the University District
neighborhood, the user then has a high probability of visiting the
Fremont neighborhood. The source in this example consists of a
sequence of two contexts, for the two neighborhoods.
[0096] In one embodiment, the recommendation agent module 140
represents users' general preferences using transition rules. For
example, a general love of antique shops is represented by a
transition rule where the source is the null context (i.e.,
anywhere, anytime) and the destination is the antique shop
category. Thus, the recommendation agent module 140 can manage a
user's general preferences by including such transition rules in
the user's routine model, and these preferences will both generate
recommendations (just as for other rules) and modify the weights
assigned to generated recommendations.
[0097] In one embodiment, the routine module 610 stores the routine
model in recommendation agent storage 650 for future further
processing. In another embodiment, the routine model is passed to
the agent compiler module 630 and used to complete the building of
a recommendation agent in combination with additional factors
(e.g., a personality model, as described below). In this case, the
routine model may not be independently saved to any form of long
term storage at all.
Modeling User Personality
[0098] The personality module 620 takes a user history comprising
slices as input and generates a model describing the user's
personality. The goal of the user personality model is to provide a
better understanding of the user in general terms (rather than
tracking specific preferences) and use that to guide the
recommendation process. Much research has been done on the problem
of categorizing, detecting, and understanding personality types.
Examples include Jung's "Psychological Types", the Myers-Briggs
Type Indicator derived from it, Enneagrams, and others. The aim of
these approaches is to provide a system for exhaustively describing
personality types along with a method to determine a person's type,
typically based on an interview or survey. Similarly, embodiments
of the personality module 620 use plurality of personality traits
(dimensions) and determine an estimate for the user's affinity with
that trait. In one such embodiment, this process is automated
(requiring no human interpretation), and nonintrusive (e.g.,
avoiding explicit survey questions).
[0099] A specific representation of user personality and method of
characterization that is well-suited to use by the personality
module 620 is described below. However, the techniques described
below are equally applicable to other personality representations,
such as those mentioned above or others that may be devised.
[0100] The context refiner module 120 outputs slices indicating
historical contexts associated with a user. The personality module
620 processes the slices and recommends other places, businesses,
or events to the user based on the user's historical contexts. In
one example, the personality module 620 builds a personality model
for the user that indicates the user's affinity with various
personality traits, and the system makes recommendations based on
the user's personality traits indicating high likelihood of
interest in the recommended item. For example, if the user's
personality model indicates an affinity for outdoor activities, the
model built by the personality module 620 may cause the system to
recommend a nearby park, even if the user has not previously been
observed visiting parks.
[0101] The personality module 620 represents each personality trait
as a position on a personality dimension, ranging from not
exhibiting the trait in question at all, to completely exhibiting
the trait at all times, with most people falling somewhere in the
middle. Thus, a given user will be evaluated as having a score
(e.g., from 0% to 100%) for each dimension. In one embodiment, the
following dimensions are used: [0102] Desire for novelty: how often
the user chooses to try new, unfamiliar experiences as opposed to
sticking with the tried and true. For example, a person with a high
novelty preference will often try new restaurants in an area, even
if they might not be as good as a nearby familiar favorite. [0103]
Extravagance: how often the person is willing to indulge in
expensive products or experiences. [0104] Preference for proximity:
to what extent the person prefers spending time near to home or
other frequently-visited places like his workplace, rather than
traveling long distances. Proximity can alternatively be stated as
its inverse, willingness to travel. [0105] Love of the outdoors:
how often the person prefers outdoor activities when available.
[0106] Preference for physical activity: how much the user prefers
physically active as opposed to sedentary activities. [0107] Desire
for solitude: how often the person prefers solitary activities as
opposed to social ones.
[0108] In other embodiments, the personality module 620 uses
different and/or additional personality dimensions. Any aspect of
personality can be used in the personality model, as long as (1) it
is known how to test for that trait in people (e.g. using survey
questions); and (2) it is known how to map the trait to useful
features in an application of the system. The exemplary embodiment
described uses only personality traits closely related to the task
of recommending places to go, but other types of traits can be
used. For example, in one embodiment, Myers-Briggs traits are used
to learn where users stand on dimensions including
extroversion/introversion and thinking/feeling. Another embodiment
learns personality traits including tastes in books, movies, and
other entertainment and thus is able to determine correlations
between activities that are not obviously linked. For example, one
such personality model can predict whether a person who likes a
certain type of movie will enjoy going to a certain restaurant.
[0109] As with other personality approaches (e.g., Myers-Briggs),
the example personality dimensions described above can be
determined for a person by asking them to answer a questionnaire.
Such questionnaires typically pose binary choices such as "would
you rather visit a library or a bar?" By asking many of these
questions, general tendencies can be determined such as `desire for
solitude` or `love of the outdoors.` However, in order to determine
users' personality traits without having to administer an intrusive
and tedious questionnaire, embodiments analyze the behavior data
collected by the system to automatically determine the personality
traits.
[0110] In one embodiment, the personality module 620 is initialized
using a baseline user group. Thus, when the system is used by an
end user, it has already been trained and can immediately provide
meaningful recommendations. In one embodiment, the following
general algorithm is used to initialize the personality module 620:
[0111] Recruit a baseline user group. [0112] Administer a
personality questionnaire to each member of the baseline user
group, thus determining their personality dimensions. [0113] Record
behavior data for the baseline user group as they go about their
everyday lives (e.g., using the system described herein) and encode
the collected behavior data as a set of features. [0114] Encode the
collected data as a series of classification problems, where each
member of the baseline group is a data point, the corresponding
behavior data are the features, and each member's place on a
personality dimension is the classification to be learned. [0115]
Use machine learning algorithms to develop a model that can predict
the position in each personality dimension of any given user based
on the behavior data of that user.
[0116] The above generalized algorithm can be realized in a number
of ways. One embodiment is described herein as an illustration.
AMAZON's "Mechanical Turk" product is a way to recruit and pay
users. A task is created in Mechanical Turk that asks users for (1)
authorization to their FOURSQUARE data; and (2) answers to a number
of binary-choice personality questions.
[0117] FOURSQUARE is a service that encourages users to "check in"
to places they visit. Users can see other users' tips about those
places, receive special deals from businesses they check in to, and
earn "badges" for checking into various kinds of places. FOURSQUARE
provides an approximation of the user's behavior. Thus, FOURSQUARE
provides a good source of data to use as some or all of the
baseline group behavior data. While such behavioral data is not as
complete as information collected by the system described herein,
since users do not check in everywhere they go and do not indicate
how long they stayed, it allows the collection of behavior data
from many existing users without having to recruit people to use a
new application for days or weeks.
[0118] The personality questionnaire given to the baseline user
group comprises binary-choice questions, as described above, which
pose a number of choices intended to elicit information about the
user's preferences. By asking enough questions (e.g., 20 questions,
but more or fewer may be asked), it can be determined where the
user stands on the personality dimensions to be used. The
Foursquare history of each baseline user group member can be used
to personalize the questions asked--the corresponding FOURSQUARE
histories reveal the city each member lives in, the neighborhoods
they spend time in, and specific places they have been. Thus, the
questions posed to each member can relate to real places around the
member, and be tailored to test traits such as novelty and
proximity that are dependent on the member's behavior over a period
of time. For example, determining a member's affinity for novelty
requires not just knowledge that the member has visited a
particular restaurant, but also whether the user has previously
visited that restaurant. Other traits can determined based on
general data about places visited by the member. For example, the
member's affinity for outdoor activities can be influenced by
identifying a visit to a park, regardless of past behavior.
[0119] Once behavior data and personality data from the baseline
user group has been collected, it is encoded to be fed to a
learning algorithm. Each member's behavior is represented as a
series of visits, each identifying features of the visit that are
relevant to the personality traits that the system is attempting to
learn. For example, in the embodiment currently being described,
each visit to a place is encoded by the following features: [0120]
Category. E.g., restaurant, Chinese restaurant, park, office, and
the like. Note that places may have multiple classifications, and
that the categories are hierarchical--multiple categories are
encoded, and include the "ancestor" categories as well (e.g.
"restaurant" is an ancestor of "Chinese restaurant"). [0121]
Distance. Based on the member's history, one or more "home areas"
are identified where the member spends a great deal of time.
Typically, these would represent home and work neighborhoods. For
each place, the distance to the nearest home area is computed.
Also, the distance from the previous place in the history is
recorded, if they are close enough in time (to track how far the
member is willing to travel from place to place). [0122]
Familiarity. How often the member has been there before. [0123]
Price range. Many local data sites (such as YELP, or CITYSEARCH)
already encode this data, for example on a scale from 1 to 4.
[0124] Once the visits are encoded, summary statistics about the
corresponding features can be computed for each member of the
baseline group. Most members' data will be dominated by the time
they spend at home and work, so counting these visits could confuse
some personality traits. For example, if one counts home and work,
most people will probably be seen to visit familiar locations most
of the time. Thus, by identifying these special places and
excluding them from certain statistics (or giving them reduced
weighting), visits to such places are prevented from dominating the
analysis. In one embodiment, the computed statistics include:
[0125] Category frequency. For each category, how often the member
goes there. Home is excluded. [0126] Distance. Median, mode, mean,
and standard deviation of distance from visited places to the
nearest home area are included as well as distance to the previous
place, if applicable. Home and work are excluded. [0127]
Familiarity. Median, mode, mean, and standard deviation of the
number of previous visits are computed. Home and work are excluded.
[0128] Price. Median, mode, mean, and standard deviation of price
for places that have price information are computed, excluding home
and work. [0129] Home. The proportion of home visits to total
visits, and average number of home visits per day are computed.
[0130] Work. As with home, proportion of work visits and average
work visits per day are computed.
[0131] Note that in this embodiment, no explicit features pertain
to outdoors, solitude, or activity; these features are inferred
from the place categories. Rather than trying to hand-code a
mapping from categories to these traits, the learning algorithm
discovers them.
[0132] Once each member is encoded as a set of features and a set
of personality classifications has been determined, standard
machine learning algorithms can be applied to learn a model that
predicts the latter from the former. Each personality dimension is
treated as an independent class to be learned. The result of this
process is a predictive model for each personality dimension; the
models take the above computed statistics as input and output
estimates of a user's affinity with each personality trait. Thus,
the personality module 620 has been initialized using the baseline
user group. Once the personality module 620 has been initialized,
an end user's history can be used to predict the user's
personality, without the need for the end user to explicitly
provide data. For example, by feeding the history of the end user
into the personality module 620, the above statistics can be
computed and the predictive model used to generate a personality
model for the end user, without requiring the end user to complete
a survey, or actively provide personality data in any other
manner.
[0133] In one embodiment, the personality module 620 stores the
personality model in recommendation agent storage 650 for future
further processing. In another embodiment, the personality model is
passed to the agent compiler module 630 and used to complete the
building of a recommendation agent in combination with additional
factors (e.g., a routine model, as described above). In this case,
the personality model may not be independently saved to any form of
long term storage at all.
[0134] The recommendation agent module 140 can use the user's
personality model to improve recommendations in many ways,
including, but not limited to: [0135] Suggesting social events and
venues to people with a low "solitude" score. [0136] Recommending
familiar places to people with a low "novelty" score when they have
not been to the place in a while or when the place is having some
kind of special event or offer. [0137] Make more recommendations
(and present them more aggressively) to people with a higher
"novelty" score. [0138] Make a whole sequence of related
recommendations (e.g., dinner, a walk, and a movie) in an
interesting neighborhood to people with a low "proximity" score.
[0139] Give social rationales (e.g., "your friends like it") more
weight when making recommendations to people with a low "solitude"
score. [0140] Balance social events with solitary recommendations
(e.g., "why not take a walk in the park after this party?") for
people with a high "solitude" score. [0141] When recommending a new
place to people with a low "novelty" score, provide rationales that
refer to familiar experiences (e.g., "this restaurant is owned by
the same people as [this other place] you visit often"). [0142] For
people with a low "extravagance" score, balance extravagant
recommendations with ways to save money (e.g., "this expensive
restaurant is offering a Tuesday dinner special").
[0143] As described previously, general preferences can be
incorporated into the user's routine model by creating transition
rules with the null source. Similarly, personality traits can be
represented by a transition rule with a null source. For example, a
preference for solitary activities can be represented by a
transition from the null source to a "solitary" context. Venues and
events can be associated with particular personality traits (e.g.,
an expensive downtown restaurant can be tagged as extravagant), and
these associations with traits become additional venue attributes
that can be used when generating and weighting recommendations for
a user. In one embodiment, some traits, such as extravagance and
outdoors are computed from available venue metadata, and are thus
the same for all users. Other traits, like novelty and proximity
are determined from information about the user's history and
location (e.g., a venue is considered novel if the user's history
does not include a previous visit to the venue) and can thus differ
from user to user. In other embodiments, these attributes are
determined for venues and events in various other and/or additional
ways.
Compiling Recommendation Agents
[0144] The agent compiler module 630 builds a customized
recommendation agent for an end user that takes the end user's
current context(s) as input and outputs customized recommendations.
In various embodiments, the recommendation agent is based on the
end user's routine model, personality model, and/or additional data
describing the end user and his/her interests.
[0145] At a high level, an agent is simply a system capable of
adapting itself to a particular user's preferences and needs. This
definition refers to how the system is presented to the user and is
not intended to imply how the agent is implemented. For example, a
monolithic system may compute recommendations for all users
simultaneously, while the visible user interface presents those
recommendations as coming from the user's agent. In one embodiment,
a customized recommendation agent is a file that is generated by
the agent compiler module 630 at a recommendation service provider
server and transferred (e.g., via the internet) to the end user's
mobile device, where it is used by a recommendation application to
provide customized recommendations. In other embodiments, the agent
compiler module 630 executes on the end user's mobile device using
data from local storage and/or accessed remotely via a network.
Updating Recommendation Agents
[0146] One limitation of many recommendation systems is that they
have trouble determining when a particular observation should or
should not influence the general model of a user's preferences. For
example, a user may be observed to visit a fast food restaurant
every day for lunch. However, in general, he dislikes fast food and
only goes there for lunch because it is fast and cheap. For a
recommendation system with no concept of context or routine, these
visits will lead to the system recommending fast food at other
times, when they would be undesirable. The system described herein
addresses this issue by learning that fast food visits happen only
in a certain context: when the user is at work, and when the user
takes a short lunch. Thus, the system is far less likely to
recommend fast food at other times. Note, however, that it is too
simple to say the user "does not like fast food"; in fact, the user
is willing to eat fast food in certain circumstances, and the
system has learned this. There may be some situation in the future
in which the user needs a quick, cheap meal, and the system will be
able to suggest a fast food restaurant.
[0147] In order to address this, the feedback module 640 collects
feedback regarding recommendations from the end user and uses that
feedback to update the end user's recommendation agent. A variety
of feedback can be collected from end users in order to improve the
models used by the recommendation agent and/or directly improve the
recommendation agent itself. At a high level, feedback can be
categorized as either explicit or implicit.
[0148] Explicit feedback is any information deliberately provided
by the end users about their preferences or the quality of a
recommendation. In various embodiments, several forms of explicit
feedback are collected and used to improve the recommendation
agents.
[0149] First, end users can explicitly express a preference in
favor of venues and events anywhere they see them in the
recommendation system's user interface (UI), for example by
selecting a button associated with the venue or event in the UI.
This includes at the time the recommendation is initially offered.
Naturally, the feedback module 640 can use this information
directly, for example by favoring recommendation of a previously
liked venue in an appropriate context. Alternatively or
additionally, the feedback module 640 can extract attributes of the
venue (such as category of business, neighborhood, price range, and
other features) and use them to increase the likelihood that the
recommendation agent of the end user will recommend items with such
attributes in future. Furthermore, the feedback module 640 can
determine how the venue aligns with the end user's personality
traits (for example, whether it is social, extravagant, and so on)
and adjust the weights of those traits in the end user's
personality model accordingly.
[0150] Second, end users can explicitly accept or reject the
reasons associated with recommendations. Reasons describe the
system's justifications for recommending something, and can be
determined by venue attributes (e.g., it's popular or highly rated,
it's from a category you've enjoyed in the past, etc.), by context
(e.g., it's nearby), or by personality traits (e.g., it's social).
Reasons also reflect the routine contexts used to generate the
recommendation. For example, if a recommendation was generated
because of past behavior in the same neighborhood, at the same time
of day, or following the same type of venue, these would all appear
as reasons ("you usually go out for coffee when you're in this
neighborhood"). By liking or rejecting these reasons for a
recommendation, end users tell the system to give more or less
weight to similar future recommendations or to those routine
contexts. Thus, the feedback module 640 adjusts the weights of
those factors accordingly. In the above example of the end user who
often goes to fast food restaurants, though he does not really like
them, the end user might be offered a couple of reasons for a fast
food recommendation: "You often go out for fast food" and "You
often go here from work". If the user rejects the former and
accepts the latter, indicating to the system that, while this
recommendation might be appropriate for this context, it does not
reflect a general category preference, the feedback module 640
adjusts the user's recommendation agent correspondingly, and thus,
fast food recommendations are less likely to be made outside of the
"work" context in future.
[0151] Third, end users can reject recommendations, requesting that
the venue or event not be shown again. In some embodiments, the
feedback module 640 uses this information to adjust the weights of
the venue, of other venues with similar attributes, and of
corresponding personality traits. In this case, the weights are
adjusted to reduce the probability of similar recommendations in
the future. In one such embodiment, a penalty is also applied
against all of the reasons associated with the rejected
recommendation, reducing the weights of those routine contexts or
other factors. For example, the above mentioned fast-food-eating
user could simply reject any fast food recommendation, which would
result in a general penalty for that category of venue. In other
embodiments, the feedback module 640 responds to rejection of a
recommendation by adding the recommended venue to a blacklist,
thereby ensuring the same recommendation is guaranteed to not be
made in future.
[0152] Fourth, users can answer questions about their preferences
and experiences. The feedback module 640 can create questions that
probe user preferences in various ways. One example is a "this or
that" question, which asks users to choose between two possible
venues. The venues are chosen as examples of opposing preferences
(e.g. extravagant vs. thrifty, solitary vs. social, Chinese vs.
Mexican food), and each question answered provides a small piece of
evidence regarding the user's preferences. Other questions include
asking whether the user enjoyed a past experience that has been
observed by the system (e.g., "how was your experience at Joe's Bar
yesterday?"--this can be treated similarly to receiving explicit
feedback in other ways for that venue), asking them to choose
between two activities (e.g. dinner and a movie vs. drinks and
dancing), and other personality questions as described above.
[0153] Implicit feedback is any information collected about the
user's preferences based on actions the user takes for other
purposes. In various embodiments, several forms of implicit
feedback are used to refine the recommendation agents.
[0154] First, if the user follows a recommendation when it is made,
this can be interpreted as a very strong endorsement of the
recommendation's suitability for the user in the user's current
context. Correspondingly, in one embodiment, the feedback module
640 reinforces the attributes of the venue and personality traits
used to select the recommendation. Additionally, by following the
recommendation, the user has implicitly provided evidence that this
particular moment in the user's routine is one when the user is
open to suggestions. For example, if the recommendation was made on
a Friday evening, the system might make more Friday recommendations
in the future.
[0155] Second, when users receive recommendations, they can add the
venue or event to their "plans", a collection of places they intend
to go to in the future. While not as strong as actually following
the recommendation, this is a strong signal that the recommendation
was a good one and that the user found value in receiving it,
though it may not be appropriate for that particular moment (and
the corresponding contexts). Correspondingly, in one embodiment,
the feedback module 640 reinforces the attributes of the venue and
personality traits used to select the recommendation, but by a
lesser amount than if the user followed the recommendation.
[0156] Third, whether or not users add recommendations to their
plans, they may eventually go to that venue or event. Again, this
provides evidence that the recommendation was a good one for that
user, though the user may be going to the venue for reasons other
than those that led the system to recommend it. Correspondingly, in
one embodiment, the feedback module 640 reinforces the attributes
of the venue and personality traits used to select the
recommendation, but by a lesser amount than if the user followed
the recommendation or added the recommendation to the user's
plans.
[0157] In some embodiments, the feedback module 640 incorporates
feedback that indicates a user's preferences or affinity for a
particular type of recommendation by adding null-source transition
rules to the model or models used by the recommendation agent. The
feedback module 640 weights updates determined from user feedback
relative to updates from observed slices. For example, if the user
visits a coffee shop, the "null to coffee shop" rule is updated in
the model to make future recommendations of that coffee shop, and
of coffee shops in general more likely. If the user then provides
explicit positive feedback for that coffee shop, the system may
count the feedback as equal to a visit or worth more or less weight
than the visit itself. In one such embodiment, explicit feedback is
weighted to be equivalent to multiple observations (e.g., 10),
because (1) explicit feedback is known to be correct (whereas
observations may be wrong); and (2) the fact that the user bothered
to provide feedback indicates that the information is important to
them.
Exemplary Methods of Making Recommendations
[0158] FIG. 7 illustrates a method of making a recommendation based
on a user's routine model, according to one embodiment. The steps
of FIG. 7 are illustrated from the perspective of the
recommendation agent module 140 performing the method. However,
some or all of the steps may be performed by other entities and/or
components. In addition, some embodiments may perform the steps in
parallel, perform the steps in different orders, or perform
different steps.
[0159] In the illustrated embodiment, the method begins with the
recommendation agent module 140 receiving 710 a set of slices
describing the user's past contexts, such as those produced by the
context refiner module 120, and stored in contextual history
storage 130. The recommendation agent module 140 then analyzes 720
the received slices to identify a set of transitions between
contexts and uses the set of transitions to build 730 a routine
model for the user, as described above with reference to FIG.
6.
[0160] The recommendation agent module 140 identifies 740 one or
more recommendations from the recommendation corpus 660 based on
the user's current context and the transition rules in the user's
routine model. To do this, the recommendation agent module 140
identifies transition rules in the user's routine model with a
source that matches the user's current context. Recall that most
real-life situations can be described by multiple contexts at
varying levels of specificity; accordingly, the recommendation
agent module 140 identifies transition rules that match the user's
current neighborhood, current time, current venue, current venue
category, and the like, as well as combinations of these contexts.
The recommendation agent module 140 then finds the destinations of
these transition rules and instantiates them to identify 740 one or
more recommendations, where instantiation involves finding a
recommendable item (e.g., a venue, an event, or the like) in the
corpus 660 with associated contexts that match the destination
contexts of at least one of the transition rules. The
recommendation agent module 140 assigns each of these
recommendations a weight based on the probability of the rule or
rules used to generate it. Additionally, the weight may be adjusted
based on venue attributes and the user's known preferences.
[0161] For example, the user may currently be at a coffee shop in
the Capitol Hill neighborhood, at 5:00 PM. Applicable rules say
that the user often goes to the University District from Capitol
Hill, that he often goes to an ice cream place after getting
coffee, that he often goes to the supermarket at around 5:00 PM,
and that he often heads home around 6:00 PM. Instantiating these
rules, the recommendation agent module 140 identifies 740
corresponding recommendations, for example, a park in the
University District, an ice cream shop in Capitol Hill, an ice
cream shop on the way home, and a supermarket on the way home. The
recommendation agent module 140 assigns a weight to each
recommendation based on the rules that caused them to be identified
740. For example, in the case of the second ice cream shop, the
weight is determined from both the "coffee then ice cream" rule and
the "go home around this time" rule. In some embodiments, the
weights are adjusted based on venue attributes; for example, if the
first ice cream shop is highly-rated by other users, the
recommendation agent module 140 gives it a boost. Finally, the
recommendation agent module 140 can adjust the weights based on the
user's preferences. For example, if the user enjoys going to parks,
the park recommendation can be given a boost, even though none of
the rules used in generating the recommendations explicitly mention
parks.
[0162] Once the recommendation agent module 140 has identified 740
one or more recommendations, the recommendation agent module
selects 750 one of the recommendations based on the corresponding
weights. In one embodiment, the recommendation agent module 140
selects the recommendation with the highest weight. In another
embodiment, the recommendation agent module 140 assigns a
probability of selection to each possible recommendation based on
the corresponding weight and selects from among them using a random
number generator. In further embodiments (not shown), more than one
of the possible recommendations is selected. For example, the
recommendation agent module 140 may select the five most highly
weighted recommendations.
[0163] Having selected 750 the recommendation (or recommendations),
the recommendation agent module 140 provides 760 the selected
recommendation for presentation to the user. In one embodiment, the
recommendation agent 140 runs on the user's mobile device, which
then presents the recommendation to the user. For example, the
recommendation can be presented visually and/or audibly by a
dedicated app executing on the user's mobile device. In another
embodiment, the recommendation agent module 140 is implemented at a
recommendation service provider's server, which provides 760 the
recommendation by sending it to the user's mobile device for
presentation. For example, the recommendation can be sent to a
dedicated app via the Internet, sent via SMS text message, sent via
multimedia message, and the like.
[0164] In one embodiment, the recommendation agent module 140
includes one or more reasons with the selected recommendation,
explaining why the recommendation was made, which is presented in
conjunction with the recommendation. For example, in the example of
a user at a coffee shop in the Capitol Hill neighborhood used
earlier, the first ice cream recommendation could be given the
reasons "You often get ice cream after getting coffee" and "This
shop is highly rated by reviewers." The supermarket could be
presented with the reasons "You often go to a supermarket at this
time" and "It's on your way home."
[0165] In one embodiment, the user's routine model can also be used
to generate recommendations for the future. In this case, the
recommendation agent module 140 uses the user's routine model to
predict a context that is likely for the user at a given time in
the future and identifies 740 recommendations based on that
context, rather than the user's current context.
[0166] FIG. 8 illustrates a method of making a recommendation based
on a user's personality model, according to one embodiment. The
steps of FIG. 8 are illustrated from the perspective of the
recommendation agent module 140 performing the method. However,
some or all of the steps may be performed by other entities and/or
components. In addition, some embodiments may perform the steps in
parallel, perform the steps in different orders, or perform
different steps.
[0167] In the illustrated embodiment, the method begins with the
recommendation agent module 140 receiving 810 a set of slices
describing the user's past contexts, such as those produced by the
context refiner module 120, and stored in contextual history
storage 130. The recommendation agent module 140 then determines
820 a set of metrics (e.g., a position of each of the personality
dimensions described above) describing the user's personality and
builds 830 a personality model for the user, as described above
with reference to FIG. 6.
[0168] The recommendation agent module 140 identifies 840 one or
more recommendations from the recommendation corpus 660 for the
user based on the user's current context and personality model. In
one embodiment, the recommendation agent module 140 identifies 840
a set of possible recommendations from the recommendation corpus
660 that are within a predetermined distance (e.g., 1 mile) of the
user's current position. In other embodiments, the possible
recommendations are identified 840 in other manners, such as by
using the user's routine model, as described above.
[0169] However the possible recommendations are determined, the
recommendation agent module 140 compares the personality metrics in
the user's personality model with metadata associated with
identified recommendations indicating the personality types that
are likely to react positively to the corresponding recommendation.
The recommendation agent module 140 then assigns each of the
possible recommendations with a weight based on how closely the
recommendation's personality profile, as indicated by the
corresponding metadata, matches that of the user.
[0170] Once the recommendation agent module 140 has identified 840
one or more recommendations, the recommendation agent module
selects 850 one of the recommendations based on the corresponding
weights. In one embodiment, the recommendation agent module 140
selects the recommendation with the highest weight. In another
embodiment, the recommendation agent module 140 assigns a
probability of selection to each possible recommendation based on
the corresponding weight and selects from among them using a random
number generator. In further embodiments (not shown), more than one
of the possible recommendations is selected. For example, the
recommendation agent module 140 may select the five most highly
weighted recommendations.
[0171] Having selected 850 the recommendation (or recommendations),
the recommendation agent module 140 provides 860 the selected
recommendation for presentation to the user. In one embodiment, the
recommendation agent 140 runs on the user's mobile device, which
then presents the recommendation to the user. For example, the
recommendation can be presented visually and/or audibly by a
dedicated app executing on the user's mobile device. In another
embodiment, the recommendation agent module 140 is implemented at a
recommendation service provider's server, which provides 860 the
recommendation by sending it to the user's mobile device for
presentation. For example, the recommendation can be sent to a
dedicated app via the Internet, sent via SMS text message, sent via
multimedia message, and the like.
[0172] In one embodiment, the recommendation agent module 140
includes reasons with the provided recommendation, explaining why
the recommendation was made. For example, if the user has a low
novelty score, a recommendation for a new restaurant may include
the reason "your friends recommend this place" and/or "this
restaurant is owned by the same people as [this other place] you
visit often."
[0173] In some embodiment, the user's personality model is updated
870 based on how the user responds to the selected recommendation.
As described above with reference to the feedback module 640 of
FIG. 6, both implicit and explicit feedback regarding the user's
response to delivered recommendations provides additional
information about the user's personality and interests. Therefore,
this feedback can be used to continuously improve the user's
personality model and, consequently, the relevance of the
recommendations provided in future.
Additional Configuration Considerations
[0174] A computer is adapted to execute computer program modules
for providing the functionality described herein. As used herein,
the term "module" refers to computer program logic utilized to
provide the specified functionality. Thus, a module can be
implemented in hardware, firmware, and/or software. In one
embodiment, program modules are stored on a storage device, loaded
into memory, and executed by a processor.
[0175] Embodiments of the physical components described herein can
include other and/or different modules than the ones described
here. In addition, the functionality attributed to the modules can
be performed by other or different modules in other embodiments.
Moreover, this description occasionally omits the term "module" for
purposes of clarity and convenience.
[0176] Some portions of the above description describe the
embodiments in terms of algorithmic processes or operations. These
algorithmic descriptions and representations are commonly used by
those skilled in the data processing arts to convey the substance
of their work effectively to others skilled in the art. These
operations, while described functionally, computationally, or
logically, are understood to be implemented by computer programs
comprising instructions for execution by a processor or equivalent
electrical circuits, microcode, or the like. Furthermore, it has
also proven convenient at times, to refer to these arrangements of
functional operations as modules, without loss of generality. The
described operations and their associated modules may be embodied
in software, firmware, hardware, or any combinations thereof.
[0177] The present invention also relates to an apparatus for
performing the operations herein. This apparatus may be specially
constructed for the required purposes, or it may comprise a
general-purpose computer selectively activated or reconfigured by a
computer program stored on a computer readable medium that can be
accessed by the computer. Such a computer program may be stored in
a computer readable storage medium, such as, but is not limited to,
any type of disk including floppy disks, optical disks, CD-ROMs,
magnetic-optical disks, read-only memories (ROMs), random access
memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards,
application specific integrated circuits (ASICs), or any type of
computer-readable storage medium suitable for storing electronic
instructions, and each coupled to a computer system bus.
Furthermore, the computers referred to in the specification may
include a single processor or may be architectures employing
multiple processor designs for increased computing capability.
[0178] As used herein any reference to "one embodiment" or "an
embodiment" means that a particular element, feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in various places in the specification are not
necessarily all referring to the same embodiment.
[0179] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0180] In addition, use of the "a" or "an" are employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
disclosure. This description should be read to include one or at
least one and the singular also includes the plural unless it is
obvious that it is meant otherwise. Upon reading this disclosure,
those of skill in the art will appreciate still additional
alternative structural and functional designs. Thus, while
particular embodiments and applications have been illustrated and
described, it is to be understood that the present invention is not
limited to the precise construction and components disclosed herein
and that various modifications, changes and variations which will
be apparent to those skilled in the art may be made in the
arrangement, operation and details of the method and apparatus
disclosed herein without departing from the spirit and scope as
defined in the appended claims.
* * * * *