U.S. patent application number 16/722444 was filed with the patent office on 2020-04-30 for weight-coefficient-based hybrid information recommendation.
This patent application is currently assigned to Alibaba Group Holding Limited. The applicant listed for this patent is Alibaba Group Holding Limited. Invention is credited to Dong XIE.
Application Number | 20200134478 16/722444 |
Document ID | / |
Family ID | 58763826 |
Filed Date | 2020-04-30 |
United States Patent
Application |
20200134478 |
Kind Code |
A1 |
XIE; Dong |
April 30, 2020 |
WEIGHT-COEFFICIENT-BASED HYBRID INFORMATION RECOMMENDATION
Abstract
Historical behavioral information of a user is retrieved, where
the historical behavioral data includes data associated to
operations performed by the user on a server. Recommended
information sets are determined based on the historical behavioral
information. A plurality of weight coefficients are generated for
the plurality of recommended information sets. A recommendation
list is determined based on the plurality of weight coefficients.
It is determined whether the recommendation list satisfies a
recommendation condition. If the recommendation list satisfies the
recommendation condition, a recommendation based on the
recommendation list is transmitted to the user device.
Inventors: |
XIE; Dong; (Hangzhou,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Alibaba Group Holding Limited |
George Town |
|
KY |
|
|
Assignee: |
Alibaba Group Holding
Limited
George Town
KY
|
Family ID: |
58763826 |
Appl. No.: |
16/722444 |
Filed: |
December 20, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15979946 |
May 15, 2018 |
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16722444 |
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PCT/CN2016/106016 |
Nov 16, 2016 |
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15979946 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/9536 20190101;
G06F 16/9535 20190101; G06N 5/02 20130101 |
International
Class: |
G06N 5/02 20060101
G06N005/02; G06F 16/9535 20060101 G06F016/9535 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 25, 2015 |
CN |
201510831206.3 |
Claims
1. A computer-implemented method, comprising: retrieving, by the
one or more processors, historical behavioral information of a
user, the historical behavioral information comprising data
associated to operations performed by the user on a server;
determining, by the one or more processors, a plurality of
recommended information sets based on the historical behavioral
information; generating, by the one or more processors, a plurality
of weight coefficients, each weight coefficient of the plurality of
weight coefficients being generated for each recommended
information set of the plurality of recommended information sets;
determining, by the one or more processors, a recommendation list
based on the plurality of weight coefficients; determining, by the
one or more processors, whether the recommendation list satisfies a
recommendation condition; and in response to determining that the
recommendation list satisfies the recommendation condition,
transmitting, by the one or more processors, a recommendation based
on the recommendation list to the user.
2. The computer-implemented method of claim 1, further comprising:
categorizing the historical behavioral information into test
information and reference information.
3. The computer-implemented method of claim 2, wherein determining
the plurality of recommended information sets comprises:
determining the plurality of recommended information sets through a
plurality of recommendation algorithms different from each other,
the test information comprising results of a portion of the
historical behavioral information analyzed by the plurality of
recommendation algorithms.
4. The computer-implemented method of claim 2, wherein whether the
recommendation list satisfies the recommendation condition
comprises: determining an accuracy of the recommendation list based
on the reference information; determining whether the accuracy is
greater than a threshold; and in response to determining that the
accuracy is greater than the threshold, determining that the
recommendation list satisfies the recommendation.
5. The computer-implemented method of claim 4, further comprising:
in response to determining that the accuracy is lower than the
threshold, adjusting, by the one or more processors, the weight
coefficient of each recommended information set of the plurality of
recommended information sets.
6. The computer-implemented method of claim 5, wherein adjusting
the weight coefficient comprises: determining a value of an
adjustment to the weight coefficient for each recommended
information set based on an iteration information that is related
to the reference information; and adjusting the weight coefficient
of each recommended information set based on the determined value
of the adjustment.
7. The computer-implemented method of claim 6, wherein the
iteration information comprises at least one of a value of a
previous adjustment of the weight coefficient of each recommended
information set, an accuracy of a recommendation list that was
previously determined, and highest accuracy value of a plurality of
accuracy values of previously determined recommendation lists.
8. The computer-implemented method of claim 7, further comprising:
in response to determining that a number of adjustments reaches an
adjustment threshold, determining a most accurate recommendation
list from the previously determined recommendation lists based on
the plurality of accuracy values; and generating the recommendation
based on the most accurate recommendation list.
9. The computer-implemented method of claim 1, wherein each weight
coefficient of the plurality of weight coefficients comprises a
sub-weight coefficient of each piece of information comprised in
the recommended information set based on a weight coefficient of
the respective recommended information set and a recommended weight
coefficient of each piece of information relative to the
recommended information set.
10. The computer-implemented method of claim 9, further comprising:
determining, for each piece of information, a sum of sub-weight
coefficients of the recommended information sets, and using the sum
as a total weight coefficient of the particular piece of
information; and determining the recommendation list based on the
total weight coefficient of each piece of information.
11. A non-transitory, computer-readable medium storing one or more
instructions executable by a computer system to perform operations
comprising: retrieving historical behavioral information of a user,
the historical behavioral information comprising data associated to
operations performed by the user on a server; determining a
plurality of recommended information sets based on the historical
behavioral information; generating a plurality of weight
coefficients, each weight coefficient of the plurality of weight
coefficients being generated for each recommended information set
of the plurality of recommended information sets; determining a
recommendation list based on the plurality of weight coefficients;
determining whether the recommendation list satisfies a
recommendation condition; and in response to determining that the
recommendation list satisfies the recommendation condition,
transmitting a recommendation based on the recommendation list to
the user.
12. The non-transitory, computer-readable medium of claim 11, the
operations further comprising: categorizing the historical
behavioral information into test information and reference
information.
13. The non-transitory, computer-readable medium of claim 12,
wherein determining the plurality of recommended information sets
comprises: determining the plurality of recommended information
sets through a plurality of recommendation algorithms different
from each other, the test information comprising results of a
portion of the historical behavioral information analyzed by the
plurality of recommendation algorithms.
14. The non-transitory, computer-readable medium of claim 12,
wherein whether the recommendation list satisfies the
recommendation condition comprises: determining an accuracy of the
recommendation list based on the reference information; determining
whether the accuracy is greater than a threshold; and in response
to determining that the accuracy is greater than the threshold,
determining that the recommendation list satisfies the
recommendation.
15. The non-transitory, computer-readable medium of claim 14, the
operations further comprising: in response to determining that the
accuracy is lower than the threshold, adjusting the weight
coefficient of each recommended information set of the plurality of
recommended information sets.
16. A computer-implemented system, comprising: one or more
computers; and one or more computer memory devices interoperably
coupled with the one or more computers and having tangible,
non-transitory, machine-readable media storing one or more
instructions that, when executed by the one or more computers,
perform operations comprising: retrieving historical behavioral
information of a user, the historical behavioral information
comprising data associated to operations performed by the user on a
server; determining a plurality of recommended information sets
based on the historical behavioral information; generating a
plurality of weight coefficients, each weight coefficient of the
plurality of weight coefficients being generated for each
recommended information set of the plurality of recommended
information sets; determining a recommendation list based on the
plurality of weight coefficients; determining whether the
recommendation list satisfies a recommendation condition; and in
response to determining that the recommendation list satisfies the
recommendation condition, transmitting a recommendation based on
the recommendation list to the user.
17. The computer-implemented system of claim 16, the operations
further comprising: categorizing the historical behavioral
information into test information and reference information.
18. The computer-implemented system of claim 17, wherein
determining the plurality of recommended information sets
comprises: determining the plurality of recommended information
sets through a plurality of recommendation algorithms different
from each other, the test information comprising results of a
portion of the historical behavioral information analyzed by the
plurality of recommendation algorithms.
19. The computer-implemented system of claim 17, wherein whether
the recommendation list satisfies the recommendation condition
comprises: determining an accuracy of the recommendation list based
on the reference information; determining whether the accuracy is
greater than a threshold; and in response to determining that the
accuracy is greater than the threshold, determining that the
recommendation list satisfies the recommendation.
20. The computer-implemented system of claim 19, the operations
further comprising: in response to determining that the accuracy is
lower than the threshold, adjusting the weight coefficient of each
recommended information set of the plurality of recommended
information sets.
Description
[0001] This application is a continuation of U.S. application Ser.
No. 15/979,946, filed on May 15, 2018, which is a continuation of
PCT Application No. PCT/CN2016/106016, filed on Nov. 16, 2016,
which claims priority to Chinese Patent Application No.
201510831206.3, filed on Nov. 25, 2015, and each application is
hereby incorporated by reference in its entirety.
TECHNICAL FIELD
[0002] The present application relates to the field of information
technologies, and in particular, to a hybrid recommendation method
and apparatus.
BACKGROUND
[0003] With the development of the information society, people are
accustomed to using the Internet for social activities such as
online shopping, online banking, and instant messaging. To improve
user experience of web site browsing and allow users to quickly
find content they want to browse, online-service providers usually
provide recommended information on websites based on user
behavioral data. For example, a music website can recommend songs
to users, and social networking websites can recommend friends to
users.
[0004] A recommendation list for recommending information is
usually calculated based on recommendation algorithms. There are
generally two types of recommendation algorithms: recommendations
based on information content and recommendations based on user
behavior. The former can directly calculate a recommendation list
without user behavioral data. However, a recommendation result in
the recommendation list cannot be customized to user preference,
and has low accuracy. The latter can calculate a relatively
accurate recommendation list but is limited because the accuracy
depends on the amount of user behavioral data. Traditionally, to
address the disadvantages of those two types of recommendation
algorithms, results of a plurality of recommendation algorithms are
usually combined to calculate a hybrid recommendation list. A
weight-coefficient-based hybrid recommendation method is most
commonly used because of advantages such as ease of combination,
convenience of combining recommended algorithms, and high
flexibility.
[0005] However, whether a recommendation list obtained by using the
weight-coefficient-based hybrid recommendation method satisfies
user preference, that is, whether a recommendation result in the
recommendation list satisfies the user's need depends on the weight
coefficients used in weighted hybrid recommendation. Traditionally,
weight coefficients used in the weight-coefficient-based hybrid
recommendation are usually set and adjusted based on observation or
heuristic experience.
[0006] Setting and adjusting weight coefficients based on
observation or heuristic experience requires a lot of time and
labor. Moreover, accuracy of the resultant recommendation list is
relatively low. In addition, the user preference can change, but
the weight coefficients that are set based on heuristic experience
may be unchanged for a relatively long time. When the user
preference changes, the existing method may not be able to provide
timely adjustment accordingly. Therefore, the recommendation list
obtained using the weight-coefficient-based hybrid recommendation
method may be difficult to satisfy the user's need.
Correspondingly, the user may need to spend a lot of time searching
to find information, which consumes network resources and increases
server pressure.
SUMMARY
[0007] Implementations of the present application provide an
information recommendation method and apparatus, so as to resolve
an existing problem of high network resource consumption and server
pressure, because a user needs to spend a lot of time searching for
useful information when a recommendation list obtained based on
manually set weight coefficients does not satisfy the user's
need.
[0008] An implementation of the present application provides an
information recommendation method, including: determining
historical behavioral information of a user; determining a
plurality of recommended information sets based on the historical
behavioral information; determining a recommendation list based on
a weight coefficient predetermined for each recommended information
set; determining whether the recommendation list satisfies a
predetermined recommendation condition; and if the recommendation
list satisfies the condition, recommending information included in
the recommendation list to the user; or if the recommendation list
does not satisfy the condition, adjusting the weight coefficient of
each recommended information set, determining a new recommendation
list based on the adjusted weight coefficient until the determined
recommendation list satisfies the recommendation condition, and
recommending information included in the recommendation list to the
user.
[0009] An implementation of the present application provides an
information recommendation apparatus, including: a behavior
determining module, configured to determine historical behavioral
information of a user; an information set determining module,
configured to determine a plurality of recommended information sets
based on the historical behavioral information; a combination
module, configured to determine a recommendation list based on a
weight coefficient predetermined for each recommended information
set; and a determining and recommendation module, configured to
determine whether the recommendation list satisfies a predetermined
recommendation condition; and if the recommendation list satisfies
the condition, recommend information included in the recommendation
list to the user; or if the recommendation list does not satisfy
the condition, adjust the weight coefficient of each recommended
information set, instruct the combination module to determine a new
recommendation list based on the adjusted weight coefficient until
the recommendation list determined by the combination module
satisfies the recommendation condition, and recommend information
included in the recommendation list to the user.
[0010] The implementations of the present application provide the
information recommendation method and apparatus. In the method, the
recommended information sets are determined through different
recommendation algorithms based on the historical behavioral
information of the user. The recommendation list is determined
based on the weight coefficient corresponding to each recommended
information set. When the recommendation list does not satisfy the
predetermined recommendation condition, the weight coefficient of
each recommended information set corresponding to the
recommendation list is adjusted, and an adjusted recommendation
list is obtained until the obtained recommendation list satisfies
the predetermined recommendation condition. Then the information in
the recommendation list is recommended to the user. In the
information recommendation method, the weight coefficient of each
recommended information set does not need to be manually set, and
can be automatically adjusted based on historical behavioral data
of the user to obtain a recommendation list that satisfies the
user's needs. Therefore, accuracy of the recommendation list can be
improved, so that the user does not need to spend a lot of time
searching for relevant information. In addition, network resources
can be saved and server pressure can be relieved.
BRIEF DESCRIPTION OF DRAWINGS
[0011] The accompanying drawings described here are intended to
provide a further understanding of the present application, and
constitute a part of the present application The illustrative
implementations of the present application and descriptions of the
implementations are intended to describe the present application,
and do not constitute limitations on the present application. In
the accompanying drawings:
[0012] FIG. 1 illustrates an information recommendation process,
according to an implementation of the present application;
[0013] FIG. 2 is a schematic structural diagram illustrating an
information recommendation apparatus, according to an
implementation of the present application; and
[0014] FIG. 3 is a flowchart illustrating an example of a
computer-implemented method for generating recommendations,
according to an implementation of the present disclosure.
DESCRIPTION OF EMBODIMENTS
[0015] In implementations of the present application, a
weight-coefficient-based hybrid recommendation method is still used
to obtain a recommendation list. A weight coefficient of each
recommended information set in the weight-coefficient-based hybrid
recommendation method can be iteratively determined through an
optimization algorithm based on historical behavioral information
of a user, and does not need to be determined based on heuristic
experience. As such, the obtained recommendation list is relatively
accurate, and the user does not need to spend additional time
searching for relevant information, which in turn, saves network
resources and relieves server pressure.
[0016] To illustrate the objectives, technical solutions, and
advantages of the present application, the following clearly and
completely describes the technical solutions of the present
application with reference to the implementations of the present
application and corresponding accompanying drawings. Apparently,
the described implementations are merely some but not all of the
implementations of the present application. All other
implementations obtained by a person of ordinary skill in the art
based on the implementations of the present application without
creative efforts shall fall within the protection scope of the
present application.
[0017] FIG. 1 illustrates an information recommendation process,
according to an implementation of the present application. The
process includes the following steps:
[0018] S101: Determine historical behavioral information of a
user.
[0019] In this implementation of the present application, a server
needs to recommend more accurate information to the user that
better conforms to the user's behavior habit, in order to save
network resources and relieve server pressure. Therefore, the
server needs to determine at least related information such as the
user's preferences or needs, and selectively recommend information
to the user based on the related information. Because such related
information can be associated with user's behavior, a website can
select recommended information based on the historical behavioral
information of the user.
[0020] In step S101 of the implementation of the present
application, the server first determines historical behavioral
information of the user. The historical behavioral information of
the user can be information corresponding to historical behavior of
the user on the server, for example, content and attributes of
information that the user browsed, content and attributes of
information that the user searched, and content and attributes of
information that the user followed or collected. It can be seen
that such information is related to the user's preference, needs,
etc. The server can determine historical data of the user in a
predetermined time period. The time period can be determined based
on needs. For example, the historical time period can be set as the
past three months.
[0021] S102: Determine a plurality of recommended information sets
based on the historical behavioral information.
[0022] In this implementation of the present application, the
server uses a weight-coefficient-based hybrid recommendation method
to determine a final recommendation list to be recommended to the
user. Therefore, after determining the historical behavioral
information of the user in step S101, the server can use different
recommendation algorithms to determine the plurality of recommended
information sets based on the historical behavioral information, so
that hybrid weighting is subsequently performed on the recommended
information sets to obtain the recommendation list. The
recommendation algorithms can be a collaborative filtering-based
recommendation algorithm, a content-based recommendation algorithm,
an association rule-based recommendation algorithm, a
knowledge-based recommendation algorithm, etc. The recommendation
algorithms used to determine the recommended information sets are
not limited for any particular implementations.
[0023] It is worthwhile to note that, a relative recommended weight
coefficient can be obtained for each piece of information in the
recommended information sets obtained through different
recommendation algorithms. That is, for each recommended
information set, each piece of information included in the
recommended information set has a recommended weight coefficient
corresponding to the recommended information set. Information
included in a recommended information set can have the same
recommended weight coefficient or different recommended weight
coefficients corresponding to the recommended information set.
[0024] For example, after obtaining historical behavioral
information of a user I, if the server uses a user-based
collaborative filtering recommendation algorithm and a
merchandise-based collaborative filtering recommendation algorithm
to separately perform calculation based on the historical
behavioral information, the server can determine two recommended
information sets: a recommendation set .alpha. and a recommendation
set .beta.. Because the two recommended information sets are
obtained through different recommendation algorithms, information
included in the two recommended information sets may not be the
same.
[0025] S103: Determine a recommendation list based on a weight
coefficient predetermined for each recommended information set.
[0026] Because each recommended information set is determined in
step S102, in the present application, the recommendation list can
be determined based on the weight coefficient predetermined for
each recommended information set. The predetermined weight
coefficient can be an initial weight coefficient predetermined
based on experience or a random initial weight coefficient obtained
based on a random function. The predetermined weight coefficient
represents an initial coefficient and may not be the weight
coefficient used to obtain the final recommendation list.
[0027] In this implementation of the present application,
determining a recommendation list based on a weight coefficient
predetermined for each recommended information set can be described
below.
[0028] First, for each recommended information set, a sub-weight
coefficient of each piece of information included in the
recommended information set can be determined based on a weight
coefficient of the recommended information set and a recommended
weight coefficient of each piece of information relative to the
recommended information set. Here, each piece of information
included in the recommended information set has a recommended
weight coefficient corresponding to the recommended information
set. Therefore, the sub-weight coefficient of each piece of
information relative to the recommended information set in the
present application can be a product of the recommended weight
coefficient of each piece of information relative to the
recommended information set and the weight coefficient of the
recommended information set.
[0029] In addition, for each piece of information, a sum of
sub-weight coefficients of the pieces of information relative to
all recommended information sets can be determined, and the sum is
used as a total weight coefficient of the particular piece of
information. Finally, the recommendation list can be determined
based on the total weight coefficient of each piece of
information.
[0030] Further, determining the recommendation list based on the
total weight coefficient of each piece of information can include
determining the recommendation list based on a descending order of
total weight coefficients of all pieces of information. The pieces
of information having larger total weight coefficients can be
preferably recommended to the user. Because the recommendation list
is determined based on the total weight coefficient of each piece
of information, the recommendation list can include information
from each recommended information set. When the recommendation list
includes a large amount of information, if all the information in
the recommendation list is recommended to the user, it may be
difficult for the user to find needed information from the
recommended information, and the user still needs to search for
information. Accordingly, in the method provided in the present
application, the recommendation list can be formed by first few
pieces of information among all the information. That is, the
pieces of information included in each recommended information set
can be ordered in descending order based on their total weight
coefficients. Then a specific number of pieces of information can
be selected according to the order to form the recommendation
list.
[0031] Continuing with the earlier described example, assume that
weight coefficients predetermined for the two recommended
information sets are (0.4, 0.6). The weight coefficients for the
recommended information set .alpha. and the recommended information
set .beta. are 0.4 and 0.6, respectively. Table 1 lists information
included in the recommended information set .alpha., recommended
information set .beta., and recommended weight coefficients of each
piece of information included in the two recommended information
sets.
TABLE-US-00001 TABLE 1 Information Recommended weight Information
Recommended weight included in the coefficient of included in the
coefficient of recommended information in recommended information
in information the recommended information the recommended set
.alpha. information set .alpha. set .beta. information set .beta.
Merchandise A 0.9 Merchandise M 0.8 Merchandise O 0.8 Merchandise A
0.6 Merchandise C 0.6 Merchandise O 0.5 Merchandise G 0.4
Merchandise G 0.3 Merchandise F 0.3 Merchandise T 0.2
[0032] In Table 1, a recommended weight coefficient of merchandise
A in the recommended information set .alpha. is 0.9, and a
recommended weight coefficient of merchandise A in the recommended
information set .beta. is 0.6; a weight coefficient of the
recommended information set .alpha. is 0.4, and a weight
coefficient of the recommended information set .beta. is 0.6.
Accordingly, a sub-weight coefficient of the piece of information
corresponding to the recommended information set .alpha. is 0.36,
and a sub-weight coefficient of the piece of information
corresponding to the recommended information set .beta. is 0.36. In
this case, a total weight coefficient of the piece of information
is 0.72, that is, a total weight coefficient of merchandise A is
0.72.
[0033] Table 2 lists total weight coefficients determined for the
merchandise.
TABLE-US-00002 TABLE 2 Information Total weight coefficient
Merchandise A 0.72 Merchandise O 0.62 Merchandise M 0.48
Merchandise G 0.34 Merchandise C 0.24 Merchandise T 0.12
Merchandise F 0.12
[0034] In Table 2, the merchandise is ordered in descending order
based on the total weight coefficients: merchandise A, merchandise
O, merchandise M, merchandise G, merchandise C, merchandise T, and
merchandise F. If the first five pieces of information are selected
to form the recommendation list, the final recommendation list
includes merchandise A, merchandise O, merchandise M, merchandise
G, and merchandise C.
[0035] S104: Determine whether the recommendation list satisfies a
predetermined recommendation condition; and if the recommendation
list satisfies the condition, proceed to step S105, or if the
recommendation list does not satisfy the condition, proceed to step
S106.
[0036] In this implementation of the present application, the
recommendation list is determined in steps S101 to S103. However,
it cannot be determined whether the information in the
recommendation list meets the user's expectation. Therefore,
whether the recommendation list satisfies the predetermined
recommendation condition needs to be further determined in the
present application. Different subsequent processing methods can be
selected based on different determination results.
[0037] In addition, whether the information included in the
recommended information list meets the user's expectation needs to
be determined based on the historical behavioral information of the
user. Therefore, in the present application, accuracy of the
recommendation list can be determined based on the historical
behavioral information of the user determined in step S101.
Further, whether the accuracy of the recommendation list is greater
than a predetermined threshold can be determined. If the accuracy
is greater than the threshold, the recommendation list satisfies
the predetermined recommendation condition, and step S105 is
performed. If the accuracy is not greater than the threshold, the
recommendation list does not satisfy the predetermined
recommendation condition, and step S106 is performed.
[0038] The number of pieces of information included in the
recommendation list and consistent with the historical behavioral
information of the user can be determined first. Then a ratio of
the number to the total number of pieces of information included in
the recommendation list is determined. The ratio is used as the
accuracy of the recommendation list.
[0039] Continuing with the earlier described example, assume that a
predetermined threshold on the server is 0.4. The historical
behavioral information of the user is: the user clicks merchandise
A, merchandise Q, merchandise R, merchandise H, merchandise M,
merchandise F, and merchandise L. The information in the
recommendation list determined in step S103 includes: merchandise
A, merchandise O, merchandise M, merchandise G, and merchandise C.
Therefore, the information included in the recommendation list and
consistent with the historical behavioral information of the user
is merchandise A and merchandise M, and the number of pieces of
consistent information is 2. Because the recommendation list
includes a total of five pieces of information, the ratio of the
number of pieces of consistent information to the total number of
pieces of information included in the recommendation list is 0.4.
Finally, the ratio (0.4) is used as the accuracy of the
recommendation list. The accuracy is not greater than the
predetermined threshold. Therefore, the recommendation list does
not satisfy the predetermined recommendation condition, and step
S106 is performed.
[0040] S105: Recommend information included in the recommendation
list to the user.
[0041] In this implementation of the present application, if it is
determined in step S104 that the recommendation list satisfies the
predetermined condition, the recommendation list can satisfy the
user's needs. Therefore, step S105 can be performed to recommend
the information included in the recommendation list to the
user.
[0042] S106: Adjust the weight coefficient of each recommended
information set, and return to step S103.
[0043] When it is determined in step S104 that the recommendation
list does not satisfy the recommendation condition, the server can
use a predetermined optimization algorithm to adjust the weight
coefficient of each recommended information set and determine a new
recommendation list based on the adjusted weight coefficient until
the determined recommendation list satisfies the recommendation
condition. Then the server recommends information included in the
recommendation list to the user.
[0044] In the present application, the weight coefficient of each
recommended information set can be automatically adjusted through
an iterative process of steps S103 to S106 to more quickly
determine accurate recommended information, save network resources,
and relieve server pressure.
[0045] The predetermined optimization algorithm can be used to
determine a value of an adjustment to the weight coefficient of
each recommended information set based on at least one of the
following iteration information: a value of the last adjustment to
the weight coefficient of each recommended information set,
accuracy of a recommendation list that is determined last time, or
a largest accuracy value among accuracy values of all obtained
recommendation lists. Then the weight coefficient of each
recommended information set is adjusted based on the determined
value of the adjustment.
[0046] In the following example of the weight coefficient
adjustment process, the used optimization algorithm is the particle
swarm algorithm. The server can use the equation
V.sub.k+1=W.sub.k.times.V.sub.k+C.sub.1.lamda.rand.sub.1.times.(Pbest-X.s-
ub.k)+C.sub.2.times.rand.sub.2.times.(Gbest-X.sub.k) to determine
the value of the adjustment to the weight coefficient of each
recommended information set, and then use the equation
X.sub.k+1=X.sub.K+V.sub.K+1 to determine an adjusted weight
coefficient of each recommended information set.
[0047] V.sub.k+1 is a value of the (K+1).sup.th adjustment to the
weight coefficient of each recommended information set. V.sub.k is
a value of the K.sup.th adjustment to the weight coefficient.
W.sub.k is an inertia weight coefficient of the K.sup.th
adjustment. C.sub.1 and C.sub.2 are predetermined constants.
rand.sub.1 and rand.sub.2 are random functions whose value ranges
are (0, 1). Pbest is a weight coefficient of each recommended
information set corresponding to the more accurate one in a
recommendation list determined after the K.sup.th adjustment and a
recommendation list determined after the (K+1).sup.th adjustment.
Gbest is a weight coefficient of each recommended information set
corresponding to the most accurate one in all determined
recommendation lists. X.sub.k is a weight coefficient of each
recommended information set corresponding to the recommendation
list determined after the K.sup.th adjustment.
[0048] It is worthwhile to note that the inertia weight coefficient
W.sub.k decreases with an increase in the number K of adjustments.
W.sub.k can be determined through the equation
W.sub.k=W.sub.s-(W.sub.s-W.sub.e).times.(K/K.sub.max).sup.2. In the
equation, W.sub.s is a predetermined initial inertia weight
coefficient and can be set based on experience. W.sub.e is a
predetermined final inertia weight coefficient and can also be set
based on experience. K is the current number of adjustments.
K.sub.max is the predetermined maximum number of adjustments.
[0049] As can be seen from the earlier described equation, if the
present adjustment is the (K+1).sup.th adjustment, Pbest in the
equation can be determined by comparing the accuracy of the
recommendation list determined last time in the iteration
information with accuracy of the recommendation list obtained after
the present adjustment. Gbest can be determined by comparing the
largest accuracy value with the accuracy of the recommendation list
obtained after the present adjustment. V.sub.k can be determined
based on the value of the last adjustment to the weight coefficient
of each recommended information set in the iteration information.
Then the value V.sub.k+1 of the present adjustment can be
determined through calculation. The weight coefficient of each
recommended information set after the present adjustment can be
determined based on the equation X.sub.k+1=X.sub.K+V.sub.K+1.
[0050] In addition, it is worthwhile to note that the optimization
algorithm used in the present application can be the particle swarm
algorithm, the genetic algorithm, the ant colony algorithm, the
annealing algorithm, etc. In the present application, only the
particle swarm algorithm is used as an example for description. The
optimization algorithm used to determine the value of the
adjustment to the weight coefficient of each recommended
information set is not limited.
[0051] After the adjusted weight coefficient of each recommended
information set is determined, a new recommendation list can be
determined based on the method described in step S103. It can then
be determined whether the new recommendation list satisfies the
recommendation condition based on the method described in step
S104. If the recommendation list satisfies the recommendation
condition, step S105 is performed to recommend information in the
recommendation list to the user. If the recommendation list does
not satisfy the recommendation condition, step S106 can be repeated
until the determined recommendation list satisfies the
recommendation condition, and step S105 is then performed.
[0052] In addition, in step S104, the accuracy of the
recommendation list is determined based on the historical
behavioral information of the user. It can be seen from steps S101
to S103 that the recommendation list is also determined based on
the historical behavioral information of the user. Therefore, in
step S104, the server determines whether a recommendation list
determined based on the information set is consistent with the
information set. It affects the accuracy of the determined
recommendation list, and leads to low accuracy reliability.
[0053] In view of this, in the present application, the historical
behavioral information of the user can be further divided into test
information and reference information before the plurality of
recommended information sets are determined in step S102, so that
the accuracy of the determined recommendation list is more
reliable.
[0054] The test information is used to determine the recommendation
list based on step S102 and step S103 in the present application.
In step S102, the plurality of recommended information sets can be
determined through different recommendation algorithms based on the
test information. Then the recommendation list is determined in
step S103.
[0055] In step S104, the reference information is compared with the
recommendation list to determine the accuracy of the recommendation
list. That is, the accuracy of the recommendation list can be
determined based on the reference information in step S104.
[0056] The reference information is also historical behavioral
information of the user but is different from the historical
behavioral information of the user used to determine the
recommendation list. Therefore, the comparison based on the
reference information can more accurately determine the accuracy of
the recommendation list, and the determined accuracy is more
reliable.
[0057] When the accuracy of the recommendation list is determined
based on the reference information, the equation
P.sub.u=R.sub.u.andgate.T.sub.u/R.sub.u can be used to calculate
the accuracy. P indicates the accuracy. R indicates a set of the
information included in the recommendation list. T indicates a set
of information included in the reference information. u indicates
accuracy for a user u. It can be seen that the recommendation list
determined based on the reference information can more accurately
reflect the amount of information that satisfies the user's needs
in the recommendation list.
[0058] For example, assume that the server divides the historical
behavioral information of the user I into test information and
reference information in step S102, and the recommendation list is
determined based on the test information in step S103. The
predetermined threshold in the present application is 0.4. The
accuracy of the information: merchandise A, merchandise O,
merchandise M, merchandise G, and merchandise C included in the
recommendation list is determined based on the reference
information (for example, merchandise A, merchandise Q, merchandise
R, merchandise H, and merchandise M). It can be seen based on the
equation P.sub.u=R.sub.u.andgate.T.sub.u/R.sub.u that P.sub.I=0.4.
In this case, the accuracy is not greater than the predetermined
threshold, and the recommendation list does not satisfy the
recommendation condition. Therefore, step S106 needs to be
repeated.
[0059] Further, when the weight coefficient of each recommended
information set is adjusted through the iteration process of steps
S103 to S106, the accuracy of the determined recommendation list is
still not greater than the predetermined threshold after a
plurality of adjustments. In this case, the server still cannot
accurately recommend information to the user after consuming a lot
of resources.
[0060] To avoid the problem that the determined recommendation list
still cannot satisfy the recommendation condition after many
adjustments, whether the number of adjustments reaches a
predetermined number of times needs to be further determined in the
present application. When the number of adjustments to the
recommendation list reaches the predetermined number of times,
information in a recommendation list determined based on Gbest is
recommended to the user. The predetermined number of times can be
the maximum number K. of adjustments.
[0061] The recommendation list corresponding to Gbest is the most
accurate one in all determined recommendation lists, and can
satisfy the user's needs to some extent. It avoids the problem that
the server still cannot accurately recommend information to the
user after consuming a lot of resources.
[0062] In addition, the recommendation method illustrated in FIG. 1
provided in the present application can be triggered by some
specific operations performed the user. For example, when the user
logs in to an account, the method illustrated in FIG. 1 can be
triggered to recommend information to the user. Certainly, the
method can also be triggered at a predetermined time interval.
Details are not repeated here for simplicity.
[0063] It is worthwhile to note that a plurality of servers can be
used to perform the recommendation method described in the present
application. For example, a plurality of servers in a content
delivery network can be used to share pressure of performing the
recommendation method.
[0064] Based on the information recommendation process illustrated
in FIG. 1, as shown in FIG. 2, an implementation of the present
application further provides an information recommendation
apparatus.
[0065] FIG. 2 is a schematic structural diagram illustrating an
information recommendation apparatus, according to an
implementation of the present application. The apparatus includes:
a behavior determining module 201, configured to determine
historical behavioral information of a user; an information set
determining module 202, configured to determine a plurality of
recommended information sets based on the historical behavioral
information; a combination module 203, configured to determine a
recommendation list based on a weight coefficient predetermined for
each recommended information set; and a determining and
recommendation module 204, configured to: determine whether the
recommendation list satisfies a predetermined recommendation
condition; and if the recommendation list satisfies the condition,
recommend information included in the recommendation list to the
user; or if the recommendation list does not satisfy the condition,
adjust the weight coefficient of each recommended information set,
instruct the combination module 203 to determine a new
recommendation list based on the adjusted weight coefficient until
the recommendation list determined by the combination module 203
satisfies the recommendation condition, and recommend information
included in the recommendation list to the user.
[0066] The information set determining module 202 is further
configured to divide the historical behavioral information of the
user into test information and reference information before
determining the plurality of recommended information sets based on
the historical behavioral information. The information set
determining module 202 is then configured to determine the
plurality of recommended information sets through different
recommendation algorithms based on the test information.
[0067] The combination module 203 is configured to: for each
recommended information set, determine a sub-weight coefficient of
each piece of information included in the recommended information
set based on a weight coefficient of the recommended information
set and a recommended weight coefficient of each piece of
information relative to the recommended information set; for each
piece of information, determine a sum of sub-weight coefficients of
the pieces of information relative to all recommended information
sets, and use the sum as a total weight coefficient of the
particular piece of information; and determine the recommendation
list based on the total weight coefficient of each piece of
information.
[0068] The determining and recommendation module 204 is configured
to: determine accuracy of the recommendation list based on the
reference information; determine whether the accuracy is greater
than a predetermined threshold; and if the accuracy is greater than
the threshold, determine that the recommendation list satisfies the
predetermined recommendation condition; or if the accuracy is not
greater than the threshold, determine that the recommendation list
does not satisfy the predetermined recommendation condition.
[0069] The determining and recommendation module 204 is configured
to: determine a value of an adjustment to the weight coefficient of
each recommended information set based on at least one of the
following iteration information: a value of the last adjustment to
the weight coefficient of each recommended information set,
accuracy of a recommendation list that is determined last time, or
a largest accuracy value among accuracy values of all obtained
recommendation lists; and adjust the weight coefficient of each
recommended information set based on the determined value of the
adjustment.
[0070] The determining and recommendation module 204 is further
configured to determine the most accurate one in all obtained
recommendation lists when the number of adjustments to the weight
coefficient of each recommended information set reaches a
predetermined number of times; and recommend information included
in the most accurate recommendation list to the user.
[0071] The information recommendation apparatus illustrated in FIG.
2 can be located on servers in various networks. There can be one
or more servers. When there is a plurality of servers, the
plurality of servers can be connected through a content delivery
network. The implementation method is not limited in the present
application.
[0072] In a typical configuration, a computing device includes one
or more processors (CPU), an input/output interface, a network
interface, and a memory.
[0073] The memory can include a non-persistent memory, a random
access memory (RAM), and/or a non-volatile memory in a computer
readable medium, such as a read-only memory (ROM) or a flash memory
(flash RAM). The memory is an example of the computer readable
medium.
[0074] The computer readable medium includes persistent,
non-persistent, movable, and unmovable media that can store
information by using any method or technology. The information can
be a computer readable instruction, a data structure, a program
module, or other data. Examples of a computer storage medium
include, but are not limited to, a parameter random access memory
(PRAM), a static RAM (SRAM), a dynamic RAM (DRAM), another type of
RAM, a ROM, an electrically erasable programmable ROM (EEPROM), a
flash memory or another memory technology, a compact disc-ROM
(CD-ROM), a digital versatile disc (DVD) or another optical
storage, a magnetic cassette tape, a magnetic tape disk storage or
another magnetic storage device or any other non-transitory medium,
which can be used to store information that can be accessed by the
computing device. As defined here, the computer readable medium
does not include transitory media such as modulated data signals
and carriers.
[0075] It is also worthwhile to note that, the terms "include",
"comprise", or their any other variant is intended to cover a
non-exclusive inclusion, so that a process, a method, an article,
or a device that includes a list of elements not only includes
those elements but also includes other elements that are not
expressly listed, or further includes elements inherent to such a
process, method, article, or device. An element preceded by
"includes a . . . " further includes, without more constraints,
another identical element in the process, method, article, or
device that includes the element.
[0076] A person skilled in the art should understand that the
implementations of the present application can be provided as a
method, a system, or a computer program product. Therefore, the
present application can use a form of hardware only
implementations, software only implementations, or implementations
with a combination of software and hardware. Moreover, the present
application can use a form of a computer program product
implemented on one or more computer usable storage media (including
but not limited to a magnetic disk memory, a CD-ROM, an optical
memory, etc.) that include computer usable program code.
[0077] The earlier descriptions are merely the implementations of
the present application, but are not intended to limit the present
application. A person skilled in the art can make various
modifications and variations to the present application. Any
modification, equivalent replacement, or improvement made without
departing from the spirit and principle of the present application
shall fall within the scope of the claims of the present
application.
[0078] FIG. 3 is a flowchart illustrating an example of a
computer-implemented method 300 for generating a recommendation,
according to an implementation of the present disclosure. For
clarity of presentation, the description that follows generally
describes method 300 in the context of the other figures in this
description. However, it will be understood that method 300 can be
performed, for example, by any system, environment, software, and
hardware, or a combination of systems, environments, software, and
hardware, as appropriate. In some implementations, various steps of
method 300 can be run in parallel, in combination, in loops, or in
any order.
[0079] At 302, historical behavioral information of a user is
retrieved. The historical behavioral information includes data
associated to operations performed by the user on a server. The
historical behavioral information can include content and
attributes of information that the user browsed, searched, followed
and/or collected. For example, the historical behavioral
information can include a list of items (e.g., merchandise) that
were searched for by the user ranked based on a number of times,
each item was searched for. The historical behavioral information
is associated to the user's preferences, interests, professional or
personal needs, and other user behavioral characteristics. In some
implementations, the historical behavioral information is retrieved
from a particular period of time (for example, most recent 3
months). In some implementations, the historical behavioral
information is retrieved based on frequency, such that only
repeated data type is included in the historical behavioral
information. From 302, method 300 proceeds to 304.
[0080] At 304, the historical behavioral information is categorized
into test information and reference information based on time
periods. The test information can include information from a first
time period (e.g., 2018). The reference information can include
results of an analyzed information from a second time period (e.g.,
2016-2017) that is before the first time period. From 304, method
300 proceeds to 306.
[0081] At 306, a plurality of recommended information sets is
determined based on the historical behavioral information. In some
implementations, the plurality of recommended information sets is
determined by applying different recommendation algorithms to the
test information. Examples of recommendation algorithms can include
a collaborative filtering-based recommendation algorithm, a
content-based recommendation algorithm, an association rule-based
recommendation algorithm, and a knowledge-based recommendation
algorithm. In some implementations, the recommendation algorithms
include a user-based collaborative filtering recommendation
algorithm and a merchandise-based collaborative filtering
recommendation algorithm. Each of the different recommendation
algorithms can use the same information as input and can generate
different recommended information sets as output. From 306, method
300 proceeds to 308.
[0082] At 308, a plurality of weight coefficients is generated for
the recommended information sets. The plurality of weight
coefficients can form an initial set of weight coefficients
determined based on experience (e.g., past analysis of user's
behavior) or a random set of weight coefficients obtained based on
a random function that generates numerical values within a set
interval, such as 0 to 1. The plurality of weight coefficients can
represent initial coefficients and can be adjusted through one or
more operations. Each weight coefficient of the plurality of weight
coefficients is generated for each recommended information set of
the plurality of recommended information sets. Each weight
coefficient includes a sub-weight coefficient for each piece of
information (e.g., item) included in the recommended information
set. The sub-weight coefficient is based on a weight coefficient of
the respective recommended information set and a recommended weight
coefficient of each piece of information for the respective
recommended information set. In some implementations, for each
piece of information, a sum of sub-weight coefficients of the
recommended information sets is determined. The sum can be used as
a total weight coefficient of a particular item (piece of
information). From 308, method 300 proceeds to 310.
[0083] At 310, a recommendation list is determined based on the
plurality of weight coefficients. In some implementations, the
recommendation list is determined based on the total weight
coefficient of each piece of information. From 310, method 300
proceeds to 312.
[0084] At 312 it is determined whether the recommendation list
satisfies a recommendation condition. In some implementations, the
verification of the recommendation condition includes determining
an accuracy of the recommendation list based on the reference
information and comparing the accuracy to a threshold. The accuracy
of the recommendation list can be defined as the ratio between the
recommendation list and the historical behavioral information of
the user. For example, a number of items that is included in both
the recommendation list and the historical behavioral information
is compared to the total number of items included in the
recommendation list to indicate a consistency between the
recommendation list and the historical behavioral information. If
it is determined that the accuracy is greater than a threshold it
is considered that the recommendation list satisfies the
recommendation.
[0085] In response to determining that the recommendation list does
not satisfy the recommendation condition (e.g., the accuracy is
lower than the threshold), from 312, method 300 proceeds to 314. At
314, the number of times the weight coefficients were adjusted is
compared to an adjustment limit. In some implementations, the
adjustment limit is selected based on computational resources to
save network resources, and minimize computational costs of the
server. If the adjustment limit is reached, a most accurate
recommendation list is determined from the previously determined
recommendation lists based on the plurality of accuracy values and
from 314 method 300 proceeds to 318.
[0086] In response to determining that the number of times the
weight coefficients were adjusted is below an adjustment limit,
from 314, method 300 proceeds to 316. At 316, the weight
coefficient of each recommended information set of the plurality of
recommended information sets is adjusted and an adjustment number
is increased by a unit. In some implementations, the adjustment of
the weight coefficient includes determining a value of an
adjustment to the weight coefficient for each recommended
information set based on an iteration information using an
optimization algorithm. The iteration information can include at
least one of a value of a previous adjustment of the weight
coefficient of each recommended information set, an accuracy of a
recommendation list that was previously determined, and the highest
accuracy value of a plurality of accuracy values of previously
determined recommendation lists. The optimization algorithm can
include a particle swarm algorithm, a genetic algorithm, an ant
colony algorithm, an annealing algorithm, or any other type of
optimization algorithm that combines analysis of past data with
evolution analysis. The weight coefficient of each recommended
information set can be adjusted based on the determined value of
the adjustment. From 316, method 300 returns to 310.
[0087] In response to determining that the recommendation list
satisfies the recommendation condition (e.g., the accuracy above
the threshold), from 312, method 300 proceeds to 318. At 318, a
recommendation based on the recommendation list is transmitted to
the user. For example, the recommendation can be automatically
displayed within an application associated to the recommendation
(e.g., a graphical user interface of a browser). From 318, method
300 proceeds to 320.
[0088] At 320, an operation associated to the recommendation (e.g.,
a purchasing order) is initiated or executed by the server,
skipping at least one operation (e.g., search) based on a user
input to minimize the time required to complete the operation
associated to the recommendation by saving computing resources.
After 320, method 300 stops.
[0089] Embodiments and the operations described in this
specification can be implemented in digital electronic circuitry,
or in computer software, firmware, or hardware, including the
structures disclosed in this specification or in combinations of
one or more of them. The operations can be implemented as
operations performed by a data processing apparatus on data stored
on one or more computer-readable storage devices or received from
other sources. A data processing apparatus, computer, or computing
device may encompass apparatus, devices, and machines for
processing data, including by way of example a programmable
processor, a computer, a system on a chip, or multiple ones, or
combinations, of the foregoing. The apparatus can include special
purpose logic circuitry, for example, a central processing unit
(CPU), a field programmable gate array (FPGA) or an
application-specific integrated circuit (ASIC). The apparatus can
also include code that creates an execution environment for the
computer program in question, for example, code that constitutes
processor firmware, a protocol stack, a database management system,
an operating system (for example an operating system or a
combination of operating systems), a cross-platform runtime
environment, a virtual machine, or a combination of one or more of
them. The apparatus and execution environment can realize various
different computing model infrastructures, such as web services,
distributed computing and grid computing infrastructures.
[0090] A computer program (also known, for example, as a program,
software, software application, software module, software unit,
script, or code) can be written in any form of programming
language, including compiled or interpreted languages, declarative
or procedural languages, and it can be deployed in any form,
including as a stand-alone program or as a module, component,
subroutine, object, or other unit suitable for use in a computing
environment. A program can be stored in a portion of a file that
holds other programs or data (for example, one or more scripts
stored in a markup language document), in a single file dedicated
to the program in question, or in multiple coordinated files (for
example, files that store one or more modules, sub-programs, or
portions of code). A computer program can be executed on one
computer or on multiple computers that are located at one site or
distributed across multiple sites and interconnected by a
communication network.
[0091] Processors for execution of a computer program include, by
way of example, both general- and special-purpose microprocessors,
and any one or more processors of any kind of digital computer.
Generally, a processor will receive instructions and data from a
read-only memory or a random-access memory or both. The essential
elements of a computer are a processor for performing actions in
accordance with instructions and one or more memory devices for
storing instructions and data. Generally, a computer will also
include, or be operatively coupled to receive data from or transfer
data to, or both, one or more mass storage devices for storing
data. A computer can be embedded in another device, for example, a
mobile device, a personal digital assistant (PDA), a game console,
a Global Positioning System (GPS) receiver, or a portable storage
device. Devices suitable for storing computer program instructions
and data include non-volatile memory, media and memory devices,
including, by way of example, semiconductor memory devices,
magnetic disks, and magneto-optical disks. The processor and the
memory can be supplemented by, or incorporated in, special-purpose
logic circuitry.
[0092] Mobile devices can include handsets, user equipment (UE),
mobile telephones (for example, smartphones), tablets, wearable
devices (for example, smart watches and smart eyeglasses),
implanted devices within the human body (for example, biosensors,
cochlear implants), or other types of mobile devices. The mobile
devices can communicate wirelessly (for example, using radio
frequency (RF) signals) to various communication networks
(described below). The mobile devices can include sensors for
determining characteristics of the mobile device's current
environment. The sensors can include cameras, microphones,
proximity sensors, GPS sensors, motion sensors, accelerometers,
ambient light sensors, moisture sensors, gyroscopes, compasses,
barometers, fingerprint sensors, facial recognition systems, RF
sensors (for example, Wi-Fi and cellular radios), thermal sensors,
or other types of sensors. For example, the cameras can include a
forward- or rear-facing camera with movable or fixed lenses, a
flash, an image sensor, and an image processor. The camera can be a
megapixel camera capable of capturing details for facial and/or
iris recognition. The camera along with a data processor and
authentication information stored in memory or accessed remotely
can form a facial recognition system. The facial recognition system
or one-or-more sensors, for example, microphones, motion sensors,
accelerometers, GPS sensors, or RF sensors, can be used for user
authentication.
[0093] To provide for interaction with a user, embodiments can be
implemented on a computer having a display device and an input
device, for example, a liquid crystal display (LCD) or organic
light-emitting diode (OLED)/virtual-reality (VR)/augmented-reality
(AR) display for displaying information to the user and a
touchscreen, keyboard, and a pointing device by which the user can
provide input to the computer. Other kinds of devices can be used
to provide for interaction with a user as well; for example,
feedback provided to the user can be any form of sensory feedback,
for example, visual feedback, auditory feedback, or tactile
feedback; and input from the user can be received in any form,
including acoustic, speech, or tactile input. In addition, a
computer can interact with a user by sending documents to and
receiving documents from a device that is used by the user; for
example, by sending web pages to a web browser on a user's client
device in response to requests received from the web browser.
[0094] Embodiments can be implemented using computing devices
interconnected by any form or medium of wireline or wireless
digital data communication (or combination thereof), for example, a
communication network. Examples of interconnected devices are a
client and a server generally remote from each other that typically
interact through a communication network. A client, for example, a
mobile device, can carry out transactions itself, with a server, or
through a server, for example, performing buy, sell, pay, give,
send, or loan transactions, or authorizing the same. Such
transactions may be in real time such that an action and a response
are temporally proximate; for example an individual perceives the
action and the response occurring substantially simultaneously, the
time difference for a response following the individual's action is
less than 1 millisecond (ms) or less than 1 second (s), or the
response is without intentional delay taking into account
processing limitations of the system.
[0095] Examples of communication networks include a local area
network (LAN), a radio access network (RAN), a metropolitan area
network (MAN), and a wide area network (WAN). The communication
network can include all or a portion of the Internet, another
communication network, or a combination of communication networks.
Information can be transmitted on the communication network
according to various protocols and standards, including Long Term
Evolution (LTE), 5G, IEEE 802, Internet Protocol (IP), or other
protocols or combinations of protocols. The communication network
can transmit voice, video, biometric, or authentication data, or
other information between the connected computing devices.
[0096] Features described as separate implementations may be
implemented, in combination, in a single implementation, while
features described as a single implementation may be implemented in
multiple implementations, separately, or in any suitable
sub-combination. Operations described and claimed in a particular
order should not be understood as requiring that the particular
order, nor that all illustrated operations must be performed (some
operations can be optional). As appropriate, multitasking or
parallel-processing (or a combination of multitasking and
parallel-processing) can be performed.
* * * * *