U.S. patent application number 15/814774 was filed with the patent office on 2019-01-24 for elicit user demands for item recommendation.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Yan Gao, QI CHENG LI, Li Jun Mei, Xin Zhou.
Application Number | 20190026815 15/814774 |
Document ID | / |
Family ID | 65023017 |
Filed Date | 2019-01-24 |
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United States Patent
Application |
20190026815 |
Kind Code |
A1 |
Zhou; Xin ; et al. |
January 24, 2019 |
ELICIT USER DEMANDS FOR ITEM RECOMMENDATION
Abstract
In an approach for eliciting user demands for item
recommendation, one or more computer processors retrieve one or
more items based on a user demand. The one or more computer
processors update the one or more items based on the user demand.
The one or more computer processors extract the one or more
representative words corresponding to the one or more items. The
one or more computer processors build a candidate item list based
on the one or more representative words. The one or more computer
processors generate one or more eliciting questions to help a user
select an item based on the candidate item list.
Inventors: |
Zhou; Xin; (Beijing, CN)
; Mei; Li Jun; (Beijing, CN) ; LI; QI CHENG;
(Beijing, CN) ; Gao; Yan; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
65023017 |
Appl. No.: |
15/814774 |
Filed: |
November 16, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15652620 |
Jul 18, 2017 |
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15814774 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0203 20130101;
G06F 16/2246 20190101; G06Q 30/0631 20130101 |
International
Class: |
G06Q 30/06 20060101
G06Q030/06; G06Q 30/02 20060101 G06Q030/02; G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for user demand recommendation, the method comprising:
retrieving, by one or more computer processors, one or more items
based on a user demand, wherein the user demand is characterized by
one or more demand vectors that are generated based on a model
trained by reviews of the one or more items; updating, by the one
or more computer processors, the one or more items based on the
user demand, further comprises: subtracting, by the one or more
computer processors, the one or more demand vectors from one or
more item vectors; and and wherein the one or more items is
characterized by the one or more item vectors and are generated
based on a model trained by reviews of the one or more items;
extracting, by the one or more computer processors, one or more
representative words corresponding to the one or more items by
matching, the one or more item vectors with one or more
representative word vectors and wherein the one or more
representative words is characterized by one or more word vectors
that are generated based on a model trained by reviews of the one
or more items; building, by the one or more computer processors, a
candidate item list based on the one or more representative words,
wherein the candidate item list is a KD-tree with the one or more
representative words as one or more parent nodes and the one or
more items as one or more leaf nodes; generating, by the one or
more computer processors, one or more eliciting questions to help a
user select an item based on the candidate item list; receiving, by
the one or more computer processors, one or more user answers based
on the one or more eliciting questions from the user; determining,
by the one or more computer processors, whether the one or more
eliciting questions match the user demand based on the one or more
user answers, further comprises: constructing, by the one or more
computer processors, a user selection path, further comprises:
determining, by the one or more computer processors, the one or
more latest user demands and one or more corresponding user
selection ranges; generating, by the one or more computer
processors, the one or more new user demands based on the
previously determined user demand and the user answer wherein the
user answer is based on the one or more additional eliciting
questions; generating, by the one or more computer processors, the
one or more new user selection ranges; and generating, by the one
or more computer processors, the user selection path based on the
one or more latest user demands, the one or more user selection
ranges, the one or more new user demands, and the one or more new
user selection ranges; determining, by the one or more computer
processors, whether the one or more range of a current node of the
user selection path is above a predefined threshold; and responsive
to determining the one or more range of the current node of the
user selection path is above the predefined threshold, determining,
by the one or more computer processors, that the one or more
additional eliciting questions does not match the user demand;
responsive to the one or more eliciting questions do not match the
user demand, updating, by the one or more computer processors, the
user demand based on the user selection path; and responsive to the
one or more eliciting questions matching the user demand,
generating, by the one or more computer processors, one or more
additional eliciting questions based on the candidate item list.
Description
BACKGROUND OF THE INVENTION
[0001] The present application generally relates to data
processing, and more specifically, to eliciting user demands for
item recommendation.
[0002] Item recommendation has becoming extremely common in recent
years, it has been utilized in a variety of areas including movies,
music, news, books, research articles, search queries, social tags,
and products or services in general. Accurate user demands are an
important prerequisites for high performance item recommendation.
Analyzing a user's profile and/or his/her historical behavior is a
widely-applied approach to mine user demands, however it typically
fails when required data is missing or insufficient. Therefore it's
still inevitable to get the demands from the user directly.
SUMMARY
[0003] According to an embodiment of the present invention, a
computer implemented method, in which at least one item is
retrieved based on a user demand and the retrieved at least one
item is updated based on the user demand. Then at least one
representative word is extracted for each of the updated at least
one item respectively. A candidate item list is further built based
on the extracted at least one representative word and at least one
eliciting question is generated to help the user select an item
based on the candidate item list.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 depicts a cloud computing environment in accordance
with an embodiment of the present invention;
[0005] FIG. 2 depicts an abstraction model layers in accordance
with an embodiment of the present invention;
[0006] FIG. 3 is a flowchart depicting operational steps of user
demand recommendation, in accordance with an embodiment of the
present invention;
[0007] FIG. 5 is a flowchart depicting operational steps of user
demand recommendation in another embodiment of the present
invention;
[0008] FIG. 4 illustrates a KD-tree for a car; and
[0009] FIG. 6 depicts a cloud computing node in accordance with an
embodiment of the present invention.
DETAILED DESCRIPTION
[0010] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0011] Some preferable embodiments will be described in more detail
with reference to the accompanying drawings, in which the
preferable embodiments of the present disclosure have been
illustrated. However, the present disclosure can be implemented in
various manners, and thus should not be construed to be limited to
the embodiments disclosed herein.
[0012] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0013] Characteristics are as follows:
[0014] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0015] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0016] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0017] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0018] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0019] Service Models are as follows:
[0020] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0021] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0022] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0023] Deployment Models are as follows:
[0024] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0025] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0026] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0027] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0028] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0029] Referring now to FIG. 1, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 1 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0030] Referring now to FIG. 2, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 1) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 3 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0031] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0032] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0033] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0034] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and user
demand eliciting and item recommending 96.
[0035] Generally speaking, a user can always explain his demands
only in natural language in their own vocabularies, referred as
`user language`. The problem is how to transform the demands from
`user language` to `item language` to enable effective item
matching and how to elicit user demand(s) for effective
recommending with less interaction.
[0036] To solve the problem mentioned above, an embodiment
according to present invention is described herein below with
reference now to FIG. 3 which is a flowchart illustrating an
exemplary method 300 according to an embodiment of the present
invention. At block 301, at least one item is retrieved based on a
user demand. The user demand can be presented by a demand vector,
while the item can be characterized by an item vector. The demand
vector and the item vector are generated based on a representation
model, and the representation model can be trained by original item
reviews. It would be appreciated that, the user demand and the item
can be characterized in any other proper forms, which is not
limited to vectors. The item can be a product such as a car, a
movie or a service such as a booking service etc. Usually, there
are many item reviews on the internet, one example of an item
review is "Type A car is so cool, fancy and fashion, it's suitable
for young couples without children". Item reviews are crawled and
then parsed for example using Lucene Tokenizer Algorithm or Solr
Tokenizer Algorithm or other proper algorithms in the art. The
parsed item reviews are used to train a representation model, for
example using Paragraph2Vec Algorithm to generate distributed
representations for each item review, and any other proper
distributed representation learning algorithms can be used other
than Paragraph2Vec Algorithm. The trained representation model can
be a function to map each paragraph of the item review to a unique
vector, characterized by a column in matrix D and map each of the
representative word in the item review to a unique vector,
characterized by a column in matrix W. When a user query such as
"do you have a good car" is received, the user query will be parsed
and a user demand (vector) can be generated based on the parsed
user query and the trained representation model.
[0037] In the following shows an example of a word vector of a
representative word for the representation model:
Vword1=<-0.150900 0.273823-0.228213 . . . 0.217080 0.240741>,
in the word vector, the value range of each dimension of the word
vector is between (-1, 1) with each value has no physical meanings
and is only the unique identification in the vector space. Item
vectors and demand vectors are similar.
[0038] On the other hand, item reviews only for one item (such as
the car of type A) can be parsed, and an item vector can be
generated based on the parsed item reviews and the representation
model. According to one embodiment of the present invention, at
least one representative word for each of the at least one item can
be generated respectively by matching the at least one item vector
with the at least word vector. The distance (e.g., cosine distance)
between a user demand vector and an item vector is calculated
during the matching process in which if the distance is smaller
than a threshold (e.g., 0.1), the user demand vector and the item
vector will be determined as matching. Once an item vector has been
matched with a user demand, it will be recorded.
[0039] At block 303, the retrieved at least one item is updated
based on the user demand. According to an embodiment of the present
invention, the item is updated by subtracting the demand vector
from the retrieved at least one item vector. The purpose of the
updating/subtracting is to reduce the search scope of future
search.
[0040] At block 305, at least one representative word is extracted
for each of the updated at least one item respectively. According
to an embodiment of the present invention, the at least one item
can be matched with the at least one representative word by mapping
the updated at least one item vector and the at least one word
vector. After the matched at least one word vector is obtained, the
representative word corresponding to the word vector is retrieved.
If the distance (e.g., cosine distance) between a word vector and
an item vector is smaller than a predefined threshold (e.g., 0.1),
the word vector and the item can be determined as matching, and
then the representative word corresponding to the matched word
vector is extracted.
[0041] At block 307, a candidate item list is built based on the
extracted at least one representative word. In order to build the
candidate item list, KD-Tree technology can be leveraged in this
disclosure, wherein the candidate item list is a KD-Tree with the
extracted representative word(s) as its parent node(s) and items as
leaf node(s). It would be appreciated that, KD-Tree technology is
just for purpose of description, the present invention is not
limited to it and other technology adaptive for this invention,
such as Ball-Tree and OC-Tree or any other proper data structure
could also be used.
[0042] Now referring to FIG. 4, which shows a KD-Tree of the built
candidate item list for an item `car`. Based on the at least one
representative word generated for item `car` by matching the at
least one item vector with the at least word vector, a table is
built to associate a car and at least one representative word. In
the table, there is one row for each car, and one column for each
representative word. If the m-th car has the n-th representative
word, then table cell (m, n) is set to 1, otherwise table cell (m,
n) is set to 0. A KD-Tree for the item `car` can be built based on
the table. Then a mapping is built between the extracted at least
one representative word and Question for the user. The car's
KD-Tree as shown in FIG. 5 is a binary tree in which every non-leaf
node is a representative word point and represents a group of cars.
Every non-leaf node can be thought as implicitly generating a
splitting hyper-plane that divides the space into two parts, known
as half-spaces. Points to the left of this hyper-plane are
characterized by the left sub-tree of that node and points to the
right of the hyper-plane are characterized by the right sub-tree.
The hyper-plane direction is chosen in the following way: every
node in the tree is associated with one of the k-dimensions, with
the hyper-plane perpendicular to that dimension's axis. So, for
examples, if for a particular split of a the representative word-
"cool" axis is chosen, the car with the representative word--"cool"
will appear in the left sub-tree and the car without the
representative word--"cool" will appear in the right sub-tree. Then
the left sub-tree and right sub-tree will further be split by
another representative word--e.g., "kid". Each type of car is
related to several representative words such as "interaction",
"cool", "kids", and the representative words can be associated to
the questions such as Q1: "Do you have other requirements such as
interaction capability?", Q2: "Do you have other requirements such
as the car should be cool?" or Q3: "Do you have other requirements
such as often driving kids to school?" according to the mapping
built above.
[0043] Now referring back to the block 309 of FIG. 3, at this
block, at least one eliciting question is generated to help the
user select an item based on the candidate item list. Based on the
KD-Tree shown in FIG. 4, the questions Q1, Q2 and Q3 etc. can be
prompted to the user according to the user's input, which will help
the user select an item to meet his/her demand(s).
[0044] Referring to FIG. 4, one embodiment of this disclosure can
be explained by an example interaction with the user as below. This
example is described on semantic level in order to make this
disclosure to be understood easily. Wherein [User] refers to a user
input, it can be characterized as the user demand vector as above.
[System] refers to the computer system implementing the method of
one embodiment. Car i refers to the item i, for instance, Car 1 (a
hatchback car): good, fashion, compact, economy refers to car item
1 with representative words as <good, fashion, compact,
economy>, and Car 1 is a hatchback car, the representative words
can be characterized as the word vector and Car i can be
characterized as an item vector as above.
Interaction Example 1
[0045] [User]: Do you have a good car? [System]: Yes, we have. Do
you have other requirements such as interaction capability?//It's
the result of following Step 1-3. Step 1: 8 cars are selected based
on the "good" representative word matching: Car 1 (a hatchback
car): good, fashion, compact, economy Car 2 (a notchback car):
nice, interaction, fashion, mid-class, economy Car 3 (a SUV(Sport
Utility Vehicle)): wonderful, interaction, yawing, all wheel
driven, safety protection equipment, family, cool Car 4 (a SUV):
beautiful, yawing, all wheel driven, safety protection equipment,
cool Car 5 (a sports car): excellent, interaction, cool, sense of
speed, purified air, hormone, sports Car 6 (a sports car): good,
cool, sense of speed, hormone, sports Car 7 (a MPV(Multi-Purpose
Vehicle)): nice, interaction, spacious, kids, family, extensible
Car 8 (a MPV): excellent, spacious, kids, family Step 2: Vector
Subtraction (//all car vectors are subtracted by the vector of
"good"): Car 1 (a hatchback car): fashion, compact, economy Car 2
(a notchback car): interaction, fashion, mid-class, economy Car 3
(a SUV): interaction, yawing, all wheel driven, safety protection
equipment, family, cool Car 4 (a SUV): yawing, all wheel driven,
safety protection equipment, cool Car 5 (a sports car):
interaction, cool, sense of speed, purified air, hormone, sports
Car 6 (a sports car): cool, sense of speed, hormone, sports Car 7
(a MPV): interaction, spacious, kids, family, extensible Car 8 (a
MPV): spacious, kids, family Step 3: Build a KD-tree based on these
8 cars as FIG. 5, and identifies the recommends the representative
word "interaction" to form the next question. [User]: Yes, I want
the interaction capability. [System]: Great. We have Car 2, Car 3,
Car 5 & Car 7. Do you have other requirements such as the car
should be cool?//It's the result of following step 1-2 Step 1: 4
cars are selected based on the "interaction" representative word
matching: Car 2 (a notchback car): fashion, mid-class, economy Car
3 (a SUV): yawing, all wheel driven, safety protection equipment,
family, cool Car 5 (a sports car): cool, sense of speed, purified
air, hormone, sports Car 7 (a MPV): spacious, kids, family,
extensible Step 2: Using the representative word "cool" to form the
next question.
[User]: Yes
[0046] [System]: OK We have Car 3 & Car 5. Do you have other
requirements such as often driving kids to school?//It's the result
of following step 1-2 Step 1: 2 cars are selected based on the
"cool" representative word matching: Car 3 (a SUV): yawing, all
wheel driven, safety protection equipment, family Car 5 (a sports
car): sense of speed, purified air, hormone, sports Step 2: Using
the keyword "kids" to form the next question.
[User]: No
[0047] [System]: OK, Car 5 is recommended to you! 1 car are
selected based on the "kids" representative word matching: Car 5 (a
sports car): sense of speed, purified air, hormone, sports It
should be appreciated that, interaction example 1 is used to make
the skilled person in the art understand this disclosure easily,
which is not intended to limited this disclosure.
[0048] Now referring to FIG. 5, FIG. 5 shows another embodiment of
this disclosure. Sometimes, the user is not so certain of his/her
demand. When he/she answers the soliciting questions, his/her
demand may change dynamically. It may be valuable to catch such
kind of demand change and help the user efficiently.
[0049] At block 501, at least one user answer for the at least one
eliciting question is received from the user. At block 503, it can
be determined whether the at least one eliciting question matches
the user demand based on the received at least one user answer. If
the at least one eliciting question is not matched with the user
demand, it is reported that the user demand has changed. First, at
least one user answer is analyzed by constructing a user selection
path. It should be appreciated that, the user answer can be
analyzed in any other proper ways, the User Selection Path method
is only to make the skilled person understand this disclosure
easily, which is not intended to limited this disclosure. The User
Selection Path can be defined as USPu={<d1, p1, r1>, <d2,
p2, r2>, . . . , <di, pi, ri> . . . , <dn, pn, rn>},
i and n are natural numbers, di is referred to the latest user
demand estimation at node ni (e.g., a queue recording all valid
user input, each time when there is a new user input, it will be
added to the queue if there is no conflict, otherwise, it will
first solve conflict in the queue by removing conflict user input,
and then add the new user input into the queue), pi is referred to
the position of user demand vector at node ni, which can be
characterized by a n-dimensional real-valued vector, and ri is
referred to user selection range at node ni. ri is a threshold
value which is calculated in algorithm (1) as below. Alternatively,
the USPu can also be defined as USPu={<p1, r1>, <p2,
r2>, . . . , <pi, ri> . . . , <pn, rn>} or even as
USPu={p1, p2, . . . , pi . . . , pn}. The USPu can be generated
following the steps as below: determining at least one latest user
demand and at least one corresponding user selection range,
generating at least one new user demand based on previous
determined user demand and user answer to the at least one further
eliciting question, and generating at least one new user selection
range, and then the USPu can be generated based on the at least one
latest user demand and user selection range, the at least one new
user demand, and the at least one new user selection range. The
determing comprises responding to the range of the current node of
the user selection path being above a predefined threshold,
determining the at least one eliciting question being not matched
with the user demand. The following algorithm (1) explained the
details:
TABLE-US-00001 Algorithm (1)-Detecting_User Demand Change (USPu)
Let i be the length of USPu; if i = 1, do the initialization
d.sub.0 = {user_demand}; // this is the initial user demand p.sub.0
= position representation of user demand d.sub.0; // user demand
vector r.sub.0 = initial range value (e.g., 10); else Let n.sub.i
be the current node <d.sub.i, p.sub.i, r.sub.i>, n.sub.i-1 be
the previous node <d.sub.i-1, p.sub.i-1,r.sub.i-1>; d.sub.i =
d.sub.i .orgate. {new user demand}; p.sub.i = position
representation of user demand d.sub.i; if distance(p.sub.i,
p.sub.i-1) .ltoreq.r.sub.i-1, then // within the threshold
r.sub.i=r.sub.i-1/2; else if distance(p.sub.i, p.sub.i-1) >
r.sub.i-1, then // over the threshold r.sub.i=r.sub.i-1 * 2; report
user demand change; Add <d.sub.i, p.sub.i, r.sub.i> to
USPu.
[0050] In Algorithm (1), function distance calculates the distance
(e.g., cosine distance) for two vectors.
[0051] At block 505, in response to the at least one eliciting
question matching the user demand, at least one further eliciting
question is generated based on the candidate item list, as shown in
algorithm (1).
[0052] At block 507, in response to determining that the at least
one further eliciting question does not match the user demand,
updating the user demand based on the user selection path, as shown
in algorithm (1), thus the user demand is a new demand and the user
can return to the block 301 of FIG. 3 to begin a new query with the
new demand identified by the system of this disclosure.
[0053] One embodiment of this disclosure can be explained by an
example interaction with the user as below. In this embodiment, the
user demand change is catched and the demand changed is also
met.
Interaction Example 2
[0054] [User]: Do you have a good car? [System]: Yes, we have. Do
you have other requirements such as interaction capability? [User]:
Yes, I want the interaction capability. [System]: Great. We have
Car 2, Car 3, Car 5 & Car 7. Do you have other requirements
such as the car should be cool? [User]: Oh, I prefer the compact
size. (User demand Change Monitored) 2 car are selected based on
the "compact" representative word matching: Car 1 (a hatchback
car): good, fashion, compact, economy Car 9 (a hatchback car):
compact, economy, easy parking Build another KD-tree based on these
9 cars, and identifies the representative word "fashion" to form
the next question. [System]: OK We have Car 1 & Car 9. Do you
have other requirements such as a fashion car?
[User]: Yes
[0055] [System]: OK, Car 1 is recommended to you! It should be
appreciated that, interaction example 2 is used to make the skilled
person in the art understand this disclosure easily, which is not
intended to limited this disclosure.
[0056] Referring now to FIG. 6, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0057] In cloud computing node 10 there is a computer system/server
12, which can also be adapted to depict an illustrative example of
a portable electronic device such as a communication device being
applicable to implement the embodiments of the present invention,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0058] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0059] Computer system/server 12 in cloud computing node 10 is
shown in the form of a general-purpose computing device. The
components of computer system/server 12 may include, but are not
limited to, one or more processors or processing units 16, a system
memory 28, and a bus 18 that couples various system components
including system memory 28 to processor 16.
[0060] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component Interconnect
(PCI) bus.
[0061] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0062] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0063] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0064] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
[0065] The components described herein are identified based upon
the application for which they are implemented in a specific
embodiment of the invention. However, it should be appreciated that
any particular component nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0066] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0067] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0068] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0069] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0070] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0071] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0072] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0073] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0074] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the invention. The terminology used herein was chosen
to best explain the principles of the embodiment, the practical
application or technical improvement over technologies found in the
marketplace, or to enable others of ordinary skill in the art to
understand the embodiments disclosed herein.
* * * * *