CN116228282A - Intelligent commodity distribution method for user data tendency - Google Patents

Intelligent commodity distribution method for user data tendency Download PDF

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CN116228282A
CN116228282A CN202310510872.1A CN202310510872A CN116228282A CN 116228282 A CN116228282 A CN 116228282A CN 202310510872 A CN202310510872 A CN 202310510872A CN 116228282 A CN116228282 A CN 116228282A
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CN116228282B (en
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王琨
刘滔
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Hunan Weike Technology Group Co ltd
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Abstract

The invention relates to the technical field of commodity distribution, and discloses an intelligent commodity distribution method with user data tendency, which comprises the following steps: constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix; calculating to obtain a user similarity matrix, and carrying out high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, so as to construct a user enhancement similarity matrix; and determining a user preference objective function representing the user tendency, carrying out optimization solving to obtain the user preference of different paid blogs, and carrying out paid blogs distribution processing. According to the invention, a user preference objective function for representing the preference trend of similar users on the paid blogs is obtained according to the user-paid blogs preference matrix and the user enhanced similarity matrix, the user characteristic matrix and the paid blogs preference characteristic matrix are obtained through solving, the preference values of the users on different paid blogs are obtained through calculation, and the pay blogs distribution processing is realized.

Description

Intelligent commodity distribution method for user data tendency
Technical Field
The invention relates to the technical field of commodity distribution, in particular to an intelligent commodity distribution method with user data tendency.
Background
With the continuous development of the information society, people can conveniently acquire various information through the internet, but with the rapid increase of the information scale in the internet, people often need to spend more time and effort to find the information required by themselves. The recommendation system is used as an important technical means for relieving the information overload problem, and personalized recommendation is formed by mining historical behavior data of users. In the pay blog recommendation process, each blog is provided with an independent label for distinguishing the characteristics of the blog, the existing blog recommendation is mostly carried out based on the labels, but along with the increase of the index of the blog resource presentation, a large number of blogs with the same label type appear, and how to realize the refined recommendation of the pay blog under the condition is an important way for improving the user experience. Aiming at the problem, the invention provides an intelligent commodity distribution method with user data tendency, which improves the user recommendation satisfaction of paid blogs.
Disclosure of Invention
In view of the above, the present invention provides an intelligent commodity distribution method with user data tendency, which aims at: 1) According to the buying condition of a user on a paid blog and the quotation relation between the purchased paid blog and Yu Bo thereof, a user-paid blog collection matrix and a paid Fei Boke-paid blog quotation matrix are respectively constructed, by considering the problem that the preference of the user on the paid blog changes along with time, time weight is introduced when the similarity among users is calculated, namely, the larger the occupation ratio of the difference value between the time of buying the same paid blog and the time of buying the paid blog for the first time is, the worse the real-time performance of the paid blog to the user is indicated, the lower the corresponding time weight is, and the similarity among users is calculated based on the time weight and the behavior information of buying the paid blog by the user, so that the similarity measurement of different users is realized; 2) Constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix as user-paid blog preference matrices, sequentially converting the user similarity matrix into an adjacent matrix and an undirected graph, wherein the user is the vertex in the undirected graph, converting the user similarity into a high-order similarity measure combined with the weight of the adjacent user point edges by analyzing the closed triangle structure relationship among the users, further constructing a user enhanced similarity matrix, obtaining a user preference objective function representing the tendency of similar users to pay blog preferences according to the user-paid blog preference matrix and the user enhanced similarity matrix, carrying out optimization solving on the constructed user preference objective function, solving to obtain a user characteristic matrix and a paid blog preference characteristic matrix, further calculating to obtain the preference values of the users to different paid blogs, and carrying out paid blog distribution processing according to the sorting sequence of the preference values.
The invention provides an intelligent commodity distribution method for user data trend, which comprises the following steps:
s1: collecting behavior information of purchasing a paid blog by a user and reference relation information among the paid blogs, respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, and constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix;
s2: calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and behavior information of the user purchasing the paid blog, and performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, wherein the mode sequence analysis is a main implementation mode of the high-order similarity calculation;
s3: constructing a user enhancement similarity matrix according to the calculated user high-order similarity matrix and the user similarity matrix;
s4: determining a user preference objective function representing user tendency according to the constructed user enhancement similarity matrix;
s5: and carrying out optimization solving on the constructed user preference objective function to obtain the preferences of the user on different paid blogs and carrying out paid blogs distribution processing.
As a further improvement of the present invention:
optionally, in the step S1, collecting behavior information of purchasing a paid blog by a user and reference relation information between paid blogs, and constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, including:
collecting behavior information of purchasing a paid blog by a user, and referring relation information between the paid blog and the rest paid blogs, wherein the collected information is expressed in the following form:
Figure SMS_1
；
Figure SMS_2
；
wherein:
Figure SMS_3
，/>
Figure SMS_4
indicating that user i purchased a paid blog j, < >>
Figure SMS_5
Indicating that user i did not purchase pay blog j, if +.>
Figure SMS_6
Then->
Figure SMS_7
Indicating the time when user i purchased pay blog j, if +.>
Figure SMS_8
Then->
Figure SMS_9
Is empty;
n represents the total number of selected users, m represents the total number of selected paid blogs;
Figure SMS_10
，/>
Figure SMS_11
indicating that pay blog j does not reference pay blog h, < ->
Figure SMS_12
Indicating that pay blog j references pay blog h, if +.>
Figure SMS_13
Then->
Figure SMS_14
；
Respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, wherein the constructed user-paid blog collection matrix G is as follows:
Figure SMS_15
；
the constructed paid blog-paid blog reference matrix C is:
Figure SMS_16
；
wherein:
Figure SMS_17
representing a reference relationship between the paid blog 1 and the paid blog m.
Optionally, the step S1 constructs a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix, including:
constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix:
Figure SMS_18
；
Figure SMS_19
；
wherein:
Figure SMS_20
indicating the preference degree of user i for pay blogs j, < >>
Figure SMS_21
Representing the total number of references to pay blogs purchased by user i.
Optionally, the calculating in step S2 to obtain the user similarity matrix includes:
calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of purchasing the paid blog by the user, wherein the calculation flow of the user similarity matrix is as follows:
calculating time weights of different users on paid blogs:
Figure SMS_22
；
wherein:
Figure SMS_23
representing a small positive number;
Figure SMS_24
representing the time weights of user i and user u for pay blogs j;
e represents a natural constant;
Figure SMS_25
indicating the time when user i first purchased the pay blog,/->
Figure SMS_26
Representing the time when user u first purchased the pay blog, R represents a set time threshold,/->
Figure SMS_27
，/>
Figure SMS_28
；
Figure SMS_29
Representing a time difference between a last purchase of the paid blog and a first purchase of the paid blog by the user i;
calculating the similarity between different users based on the time weight of the users to the paid blogs and the user-paid blogs collection matrix:
Figure SMS_30
；
wherein:
Figure SMS_31
representing the similarity between user i and user j;
constructing a user similarity matrix S:
Figure SMS_32
；
wherein:
the values on the diagonal lines in the user similarity matrix S each represent the user' S own similarity, which is set to 1.
Optionally, in the step S2, performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, including:
performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, wherein the mode sequence analysis is a main implementation mode of the high-order similarity calculation, and the high-order similarity calculation flow is as follows:
s21: setting the values on the diagonal lines in the user similarity matrix S to 0 to obtain a matrix
Figure SMS_33
；
S22: matrix-based
Figure SMS_34
Calculating to obtain an adjacent matrix B of the user similarity matrix:
Figure SMS_35
；
Figure SMS_36
；
wherein:
Figure SMS_37
representing a hadamard product operation, representing an element-by-element multiplication operation;
t represents the transpose of the matrix;
s23: initializing a zero matrix of n rows and n columns
Figure SMS_38
；
S24: converting the adjacent matrix B into an undirected graph form, wherein the vertex of the undirected graph is the user, and the ith row or the ith column in the adjacent matrix B corresponds to the user i;
s25: vertices for user i and user u
Figure SMS_39
Traversing vertices->
Figure SMS_40
And any other vertex to obtain a plurality of closed triangle structures, wherein the closed triangle structures are +.>
Figure SMS_41
And +.>
Figure SMS_42
Calculating to obtain the closed triangle structure about +.>
Figure SMS_43
Higher order similarity of (2):
Figure SMS_44
；
wherein:
Figure SMS_45
representing a closed triangle structure->
Figure SMS_46
In relation to->
Figure SMS_47
Higher order similarity of (2);
Figure SMS_48
representing vertex +_in undirected graph>
Figure SMS_49
The weights of the edges formed;
vertex of the vertex
Figure SMS_50
All closed triangular structures with any other vertex
Figure SMS_51
The higher-order similarity of the user i and the user u is accumulated, and the accumulated result is the higher-order similarity of the user i and the user u>
Figure SMS_52
And will->
Figure SMS_53
Update to zero matrix->
Figure SMS_54
And zero matrix +.>
Figure SMS_55
Ith row and column of (b); />
S26: repeating step S25 to obtain high-order similarity between any two users, and updating the initialized zero matrix to obtain a user high-order similarity matrix
Figure SMS_56
. In the embodiment of the present invention, the similarity greater than 1 is set to 1.
Optionally, in the step S3, constructing a user enhanced similarity matrix based on the user similarity matrix and the user higher-order similarity matrix includes:
based on user similarity matrix S and user high-order similarity matrix
Figure SMS_57
Constructing a user enhanced similarity matrix +.>
Figure SMS_58
：
Figure SMS_59
；
Wherein:
Figure SMS_60
the scale threshold is represented and set to 0.8.
Optionally, determining a user preference objective function representing a user tendency in the step S4 includes:
determining a user preference objective function representing user trends
Figure SMS_61
：
Figure SMS_62
；
Wherein:
Figure SMS_63
representing correction parameters, if->
Figure SMS_64
If not 0, then->
Figure SMS_65
If->
Figure SMS_66
0, then->
Figure SMS_67
；
Figure SMS_68
Representation ofCorrection parameters, if->
Figure SMS_69
If not 0, then->
Figure SMS_70
If->
Figure SMS_71
0, then->
Figure SMS_72
，/>
Figure SMS_73
Representing user enhanced similarity matrix +.>
Figure SMS_74
Element values of the ith row and the qth column;
Figure SMS_75
representing parameters to be optimally solved, U representing a user feature matrix, V representing a pay blog preference feature matrix,
Figure SMS_76
represents the ith row, < > in the user feature matrix U>
Figure SMS_77
Representing the j-th row in the pay blog preference feature matrix V;
Figure SMS_78
representing a regularization term parameter, which is set to 0.2;
Figure SMS_79
representing the L1 norm.
Optionally, in the step S5, the optimizing and solving the constructed user preference objective function includes:
carrying out optimization solution on the constructed user preference objective function to obtain a user feature matrix and a pay blog preference feature matrix, wherein the optimization solution flow is as follows:
s51: order the
Figure SMS_80
Representing the solution parameters to be optimized, converting the user preference objective function into +.>
Figure SMS_81
And initialize +.>
Figure SMS_82
Setting the iteration number of the current algorithm as d, the initial value of d as 0, the maximum iteration number as Max, and initializing +.>
Figure SMS_83
Is a unit matrix;
s52: calculation of
Figure SMS_84
；
S53: calculating the iteration step length of the (d+1) th iteration
Figure SMS_85
：
Figure SMS_86
；
Wherein:
Figure SMS_87
representing the trace of the calculated matrix; />
S54: updating the solution parameters to be optimized based on the iteration step length:
Figure SMS_88
；
wherein:
Figure SMS_89
representing the identity matrix;
and updating the iteration step length:
Figure SMS_90
；
Figure SMS_91
；
Figure SMS_92
；
if it is
Figure SMS_93
Will->
Figure SMS_94
As the result of the optimization solution, and extract the user feature matrix +.>
Figure SMS_95
Payment blog preference feature matrix +.>
Figure SMS_96
；
No order of no
Figure SMS_97
The process returns to step S52.
Optionally, in the step S5, paid blog distribution processing is performed according to the preference of the user to different paid blogs, including:
user characteristic matrix obtained according to optimization solution
Figure SMS_98
Payment blog preference feature matrix +.>
Figure SMS_99
Calculating preference values of the user i on any paid blog j:
Figure SMS_100
；
wherein:
Figure SMS_101
representing the preference value of the user i for any paid blog j;
and pushing the paid blogs which are not purchased by the user i and have preference values larger than the preset preference threshold to the user i.
In order to solve the above-described problems, the present invention provides an electronic apparatus including:
a memory storing at least one instruction;
the communication interface is used for realizing the communication of the electronic equipment; and a processor executing the instructions stored in the memory to implement the intelligent commodity distribution method for user data trends described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-mentioned intelligent commodity distribution method for user data tendency.
Compared with the prior art, the invention provides an intelligent commodity distribution method with user data tendency, which has the following advantages:
firstly, the scheme provides a user similarity measurement mode, and a user similarity matrix is obtained by calculation according to a user-paid blog collection matrix and behavior information of purchasing paid blogs by a user, wherein the calculation flow of the user similarity matrix is as follows: calculating time weights of different users on paid blogs:
Figure SMS_102
；
Figure SMS_103
；
wherein:
Figure SMS_104
representing a small positive number; />
Figure SMS_105
Representing the time weights of user i and user u for pay blogs j; e represents a natural constant; />
Figure SMS_106
Indicating the time when user i first purchased the pay blog,/->
Figure SMS_107
Representing the time when user u first purchased the pay blog, R represents a set time threshold,/->
Figure SMS_108
，/>
Figure SMS_109
；/>
Figure SMS_110
Representing a time difference between a last purchase of the paid blog and a first purchase of the paid blog by the user i; calculating the similarity between different users based on the time weight of the users to the paid blogs and the user-paid blogs collection matrix:
Figure SMS_111
；
wherein:
Figure SMS_112
representing the similarity between user i and user j; constructing a user similarity matrix S:
Figure SMS_113
；
wherein: the values on the diagonal lines in the user similarity matrix S each represent the user' S own similarity, which is set to 1. According to the scheme, a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix are respectively constructed according to the purchasing condition of a user on a paid blog and the reference relation between the purchased paid blog and the paid blog Yu Bo, and by considering the problem that the preference of the user on the paid blog changes along with time, time weight is introduced when the similarity among users is calculated, namely, the larger the ratio of the difference value between the time of purchasing the same paid blog and the time of purchasing the paid blog for the first time in the whole time is, the worse the real-time performance of the paid blog to the user is indicated, the lower the corresponding time weight is, and the similarity among users is calculated based on the time weight and the behavior information of purchasing the paid blog by the user, so that the similarity measurement of different users is realized.
Meanwhile, the scheme provides a pay blog distribution processing mode, and the user high-order similarity matrix is obtained by carrying out high-order similarity calculation on the user similarity matrix, wherein the high-order similarity calculation flow is as follows: setting the values on the diagonal lines in the user similarity matrix S to 0 to obtain a matrix
Figure SMS_114
The method comprises the steps of carrying out a first treatment on the surface of the Based on matrix->
Figure SMS_115
Calculating to obtain an adjacent matrix B of the user similarity matrix:
Figure SMS_116
；
Figure SMS_117
；
wherein:
Figure SMS_118
representing a hadamard product operation, representing an element-by-element multiplication operation; t represents the transpose of the matrix; initializing a zero matrix of n rows and n columns>
Figure SMS_119
The method comprises the steps of carrying out a first treatment on the surface of the Converting the adjacent matrix B into an undirected graph form, wherein the vertex of the undirected graph is the user, and the ith row or the ith column in the adjacent matrix B corresponds to the user i; vertex for user i and user u +.>
Figure SMS_120
Traversing vertices
Figure SMS_121
And any other vertex to obtain a plurality of closed triangle structures, wherein the closed triangle structures are +.>
Figure SMS_122
And +.>
Figure SMS_123
Calculating to obtain the closed triangle structure
Figure SMS_124
Higher order similarity of (2):
Figure SMS_125
；
wherein:
Figure SMS_128
representing a closed triangle structure->
Figure SMS_132
In relation to->
Figure SMS_135
Higher order similarity of (2); />
Figure SMS_129
Representing vertices in undirected graph
Figure SMS_131
The weights of the edges formed; vertex->
Figure SMS_133
All closed triangular structures with any other vertex are about +.>
Figure SMS_136
The higher-order similarity of the user i and the user u is accumulated, and the accumulated result is the higher-order similarity of the user i and the user u>
Figure SMS_126
And will->
Figure SMS_130
Update to zero matrix->
Figure SMS_134
And zero matrix +.>
Figure SMS_137
Ith row and column of (c). Determining a user preference objective function representing a user's tendency>
Figure SMS_127
：
Figure SMS_138
；
Wherein:
Figure SMS_147
representing correction parameters, if->
Figure SMS_146
If not 0, then->
Figure SMS_152
If->
Figure SMS_141
0, then->
Figure SMS_151
=0；/>
Figure SMS_143
Representing correction parameters, if->
Figure SMS_154
If not 0, then->
Figure SMS_142
If->
Figure SMS_153
0, then->
Figure SMS_139
，/>
Figure SMS_148
Representing user enhanced similarity matrix +.>
Figure SMS_144
Element values of the ith row and the qth column; />
Figure SMS_149
Representing parameters to be solved optimally, U representing a user feature matrix, V representing a pay blog preference feature matrix,/for>
Figure SMS_145
Represents the ith row, < > in the user feature matrix U>
Figure SMS_155
Representing the j-th row in the pay blog preference feature matrix V; />
Figure SMS_140
Representing a regularization term parameter, which is set to 0.2; />
Figure SMS_150
Representing the L1 norm. The scheme comprises the steps of constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix as user-paid blog preference matrices, sequentially converting a user similarity matrix into an adjacent matrix and an undirected graph, wherein a user is the vertex in the undirected graph, converting the user similarity into a high-order similarity measure combined with the weight of the adjacent user point side by analyzing the closed triangle structure relationship among the users, further constructing a user enhanced similarity matrix, obtaining a user preference objective function representing the preference tendency of similar users to the paid blogs according to the user-paid blog preference matrix and the user enhanced similarity matrix, carrying out optimization solving on the constructed user preference objective function, solving to obtain a user characteristic matrix and a paid blog preference characteristic matrix, and further calculating to obtain different payment of the usersAnd (5) the preference value of the blog and carrying out pay blog distribution processing according to the ordering sequence of the preference value.
Drawings
FIG. 1 is a flow chart of a method for intelligent commodity distribution with user data trends according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an electronic device for implementing an intelligent commodity distribution method with user data tendency according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent commodity distribution method with user data tendency. The execution subject of the user data-prone intelligent commodity distribution method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the present application. In other words, the user data-prone intelligent commodity distribution method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1
S1: collecting behavior information of purchasing a paid blog by a user and reference relation information among the paid blogs, respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, and constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix.
In the step S1, the behavior information of purchasing the paid blogs by the user and the reference relation information among the paid blogs are collected, and a user-paid blogs collection matrix and a paid Fei Boke-paid blogs reference matrix are constructed, which comprises the following steps:
collecting behavior information of purchasing a paid blog by a user, and referring relation information between the paid blog and the rest paid blogs, wherein the collected information is expressed in the following form:
Figure SMS_156
；
Figure SMS_157
；
wherein:
Figure SMS_158
，/>
Figure SMS_159
indicating that user i purchased a paid blog j, < >>
Figure SMS_160
Indicating that user i did not purchase pay blog j, if +.>
Figure SMS_161
Then->
Figure SMS_162
Indicating the time when user i purchased pay blog j, if +.>
Figure SMS_163
Then->
Figure SMS_164
Is empty;
n represents the total number of selected users, m represents the total number of selected paid blogs;
Figure SMS_165
，/>
Figure SMS_166
indicating that pay blog j does not reference pay blog h, < ->
Figure SMS_167
Indicating that pay blog j references pay blog h, if +.>
Figure SMS_168
Then->
Figure SMS_169
；
Respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, wherein the constructed user-paid blog collection matrix G is as follows:
Figure SMS_170
；
the constructed paid blog-paid blog reference matrix C is:
Figure SMS_171
；
wherein:
Figure SMS_172
representing a reference relationship between the paid blog 1 and the paid blog m.
In the step S1, a user-paid blog preference matrix is constructed according to a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, which includes:
constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix:
Figure SMS_173
；
Figure SMS_174
；
wherein:
Figure SMS_175
indicating the preference degree of user i for pay blogs j, < >>
Figure SMS_176
Representing the total number of references to pay blogs purchased by user i.
S2: and calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of the user purchasing the paid blog, and performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix.
And step S2, calculating to obtain a user similarity matrix, wherein the step S comprises the following steps:
calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of purchasing the paid blog by the user, wherein the calculation flow of the user similarity matrix is as follows:
calculating time weights of different users on paid blogs:
Figure SMS_177
；
Figure SMS_178
；
wherein:
Figure SMS_179
representing a small positive number;
Figure SMS_180
representing the time weights of user i and user u for pay blogs j;
e represents a natural constant;
Figure SMS_181
indicating the time when user i first purchased the pay blog,/->
Figure SMS_182
Representing the time when user u first purchased the pay blog, R represents a set time threshold,/->
Figure SMS_183
，/>
Figure SMS_184
；
Figure SMS_185
Representing a time difference between a last purchase of the paid blog and a first purchase of the paid blog by the user i;
calculating the similarity between different users based on the time weight of the users to the paid blogs and the user-paid blogs collection matrix:
Figure SMS_186
；
wherein:
Figure SMS_187
representing the similarity between user i and user j;
constructing a user similarity matrix S:
Figure SMS_188
；
wherein:
the values on the diagonal lines in the user similarity matrix S each represent the user' S own similarity, which is set to 1.
In the step S2, performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, including:
performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, wherein the mode sequence analysis is a main implementation mode of the high-order similarity calculation, and the high-order similarity calculation flow is as follows:
s21: setting the values on the diagonal lines in the user similarity matrix S to 0 to obtain a matrix
Figure SMS_189
；
S22: matrix-based
Figure SMS_190
Calculating to obtain an adjacent matrix B of the user similarity matrix:
Figure SMS_191
；
Figure SMS_192
；
wherein:
Figure SMS_193
representing a hadamard product operation, representing an element-by-element multiplication operation;
t represents the transpose of the matrix;
s23: initializing a zero matrix of n rows and n columns
Figure SMS_194
；
S24: converting the adjacent matrix B into an undirected graph form, wherein the vertex of the undirected graph is the user, and the ith row or the ith column in the adjacent matrix B corresponds to the user i;
s25: vertices for user i and user u
Figure SMS_195
Traversing vertices->
Figure SMS_196
And any other vertex to obtain a plurality of closed triangle structures, wherein the closed triangle structures are +.>
Figure SMS_197
And +.>
Figure SMS_198
Calculating to obtain the closed triangle structure about +.>
Figure SMS_199
Higher order similarity of (2):
Figure SMS_200
；
wherein:
Figure SMS_201
representing a closed triangle structure->
Figure SMS_202
In relation to->
Figure SMS_203
Higher order similarity of (2); />
Figure SMS_204
Representing vertex +_in undirected graph>
Figure SMS_205
The weights of the edges formed;
vertex of the vertex
Figure SMS_206
All closed triangular structures with any other vertex
Figure SMS_207
The higher-order similarity of the user i and the user u is accumulated, and the accumulated result is the higher-order similarity of the user i and the user u>
Figure SMS_208
And will->
Figure SMS_209
Update to zero matrix->
Figure SMS_210
And zero matrix +.>
Figure SMS_211
Ith row and column of (b);
s26: repeating step S25 to obtain high-order similarity between any two users, and updating the initialized zero matrix to obtain a user high-order similarity matrix
Figure SMS_212
。
S3: and constructing a user enhancement similarity matrix according to the calculated user high-order similarity matrix and the user similarity matrix.
In the step S3, a user enhanced similarity matrix is constructed based on the user similarity matrix and the user higher-order similarity matrix, including:
based on user similarity matrix S and user high-order similarity matrix
Figure SMS_213
Constructing a user enhanced similarity matrix +.>
Figure SMS_214
：
Figure SMS_215
；
Wherein:
Figure SMS_216
the scale threshold is represented and set to 0.8.
S4: and determining a user preference objective function representing the user tendency according to the constructed user enhancement similarity matrix.
The step S4 of determining a user preference objective function representing a user tendency includes:
determining a user preference objective function representing user trends
Figure SMS_217
：
Figure SMS_218
；
Wherein:
Figure SMS_219
representing correction parameters, if->
Figure SMS_220
If not 0, then->
Figure SMS_221
If->
Figure SMS_222
0, then->
Figure SMS_223
；
Figure SMS_224
Representing correction parameters, if->
Figure SMS_225
If not 0, then->
Figure SMS_226
If->
Figure SMS_227
0, then->
Figure SMS_228
，/>
Figure SMS_229
Representing user enhanced similarity matrix +.>
Figure SMS_230
Element values of the ith row and the qth column;
Figure SMS_231
representing parameters to be optimally solved, U representing a user feature matrix, V representing a pay blog preference feature matrix,
Figure SMS_232
represents the ith row, < > in the user feature matrix U>
Figure SMS_233
Representing the j-th row in the pay blog preference feature matrix V;
Figure SMS_234
representing a regularization term parameter, which is set to 0.2;
Figure SMS_235
representing the L1 norm.
S5: and carrying out optimization solving on the constructed user preference objective function to obtain the preferences of the user on different paid blogs and carrying out paid blogs distribution processing.
And in the step S5, the constructed user preference objective function is optimally solved, and the method comprises the following steps:
carrying out optimization solution on the constructed user preference objective function to obtain a user feature matrix and a pay blog preference feature matrix, wherein the optimization solution flow is as follows:
s51: order the
Figure SMS_236
Representing the solution parameters to be optimized, converting the user preference objective function into +.>
Figure SMS_237
And initialize +.>
Figure SMS_238
Setting the iteration number of the current algorithm as d, the initial value of d as 0, the maximum iteration number as Max, and initializing +.>
Figure SMS_239
Is a unit matrix;
s52: calculation of
Figure SMS_240
；
S53: calculating the iteration step length of the (d+1) th iteration
Figure SMS_241
：
Figure SMS_242
；
Wherein:
Figure SMS_243
representing the trace of the calculated matrix;
s54: updating the solution parameters to be optimized based on the iteration step length:
Figure SMS_244
；
wherein:
Figure SMS_245
representing the identity matrix;
and updating the iteration step length:
Figure SMS_246
；
Figure SMS_247
；
Figure SMS_248
；
if it is
Figure SMS_249
Will->
Figure SMS_250
As the result of the optimization solution, and extract the user feature matrix +.>
Figure SMS_251
Payment blog preference feature matrix +.>
Figure SMS_252
；
No order of no
Figure SMS_253
The process returns to step S52.
In the step S5, payment blog distribution processing is performed according to the preference of the user to different payment blogs, including:
user characteristic matrix obtained according to optimization solution
Figure SMS_254
Payment blog preference feature matrix +.>
Figure SMS_255
Calculating preference values of the user i on any paid blog j:
Figure SMS_256
；
wherein:
Figure SMS_257
representing the preference value of the user i for any paid blog j;
and pushing the paid blogs which are not purchased by the user i and have preference values larger than the preset preference threshold to the user i.
Example 2
Fig. 2 is a schematic structural diagram of an electronic device for implementing an intelligent commodity distribution method for user data tendency according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing intelligent commodity distribution, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
collecting behavior information of purchasing a paid blog by a user and reference relation information among the paid blogs, respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, and constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix;
calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of the user purchasing the paid blog, and performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix;
constructing a user enhancement similarity matrix according to the calculated user high-order similarity matrix and the user similarity matrix;
determining a user preference objective function representing user tendency according to the constructed user enhancement similarity matrix;
and carrying out optimization solving on the constructed user preference objective function to obtain the preferences of the user on different paid blogs and carrying out paid blogs distribution processing.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (9)

1. A method for intelligent commodity distribution of user data trends, the method comprising:
s1: collecting behavior information of purchasing a paid blog by a user and reference relation information among the paid blogs, respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, and constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix;
s2: calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of the user purchasing the paid blog, and performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix;
s3: constructing a user enhancement similarity matrix according to the calculated user high-order similarity matrix and the user similarity matrix;
s4: determining a user preference objective function representing user tendency according to the constructed user enhancement similarity matrix;
s5: and carrying out optimization solving on the constructed user preference objective function to obtain the preferences of the user on different paid blogs and carrying out paid blogs distribution processing.
2. The intelligent commodity distribution method according to claim 1, wherein in step S1, behavior information of purchasing a paid blog and reference relation information between paid blogs by a user are collected, and a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix are constructed, comprising:
collecting behavior information of purchasing a paid blog by a user, and referring relation information between the paid blog and the rest paid blogs, wherein the collected information is expressed in the following form:
Figure QLYQS_1
；
Figure QLYQS_2
；
wherein:
Figure QLYQS_3
，/>
Figure QLYQS_4
indicating that user i purchased a paid blog j, < >>
Figure QLYQS_5
Indicating that user i did not purchase pay blog j, if +.>
Figure QLYQS_6
Then->
Figure QLYQS_7
Indicating the time when user i purchased pay blog j, if +.>
Figure QLYQS_8
Then->
Figure QLYQS_9
Is empty;
n represents the total number of selected users, m represents the total number of selected paid blogs;
Figure QLYQS_10
，/>
Figure QLYQS_11
indicating that pay blog j does not reference pay blog h, < ->
Figure QLYQS_12
Indicating that pay blog j references pay blog h, if +.>
Figure QLYQS_13
Then->
Figure QLYQS_14
；
Respectively constructing a user-paid blog collection matrix and a paid Fei Boke-paid blog reference matrix, wherein the constructed user-paid blog collection matrix G is as follows:
Figure QLYQS_15
；
the constructed paid blog-paid blog reference matrix C is:
Figure QLYQS_16
；
wherein:
Figure QLYQS_17
representing a reference relationship between the paid blog 1 and the paid blog m. />
3. The intelligent commodity distribution method according to claim 2, wherein in step S1, the user-paid blog preference matrix is constructed according to the user-paid blog collection matrix and the payment Fei Boke-paid blog reference matrix, comprising:
constructing a user-paid blog preference matrix according to the user-paid blog collection matrix and the paid Fei Boke-paid blog reference matrix:
Figure QLYQS_18
；
Figure QLYQS_19
；
wherein:
Figure QLYQS_20
indicating the preference degree of user i for pay blogs j, < >>
Figure QLYQS_21
Representing the total number of references to pay blogs purchased by user i.
4. The intelligent commodity distribution method according to claim 1, wherein said calculating in step S2 to obtain a user similarity matrix comprises:
calculating to obtain a user similarity matrix according to the user-paid blog collection matrix and the behavior information of purchasing the paid blog by the user, wherein the calculation flow of the user similarity matrix is as follows:
calculating time weights of different users on paid blogs:
Figure QLYQS_22
；
wherein:
Figure QLYQS_23
representing a small positive number;
Figure QLYQS_24
representing the time weights of user i and user u for pay blogs j;
e represents a natural constant;
Figure QLYQS_25
indicating the time when user i first purchased the pay blog,/->
Figure QLYQS_26
Representing the time when user u first purchased the pay blog, R represents a set time threshold,/->
Figure QLYQS_27
，/>
Figure QLYQS_28
；
Figure QLYQS_29
Representing a time difference between a last purchase of the paid blog and a first purchase of the paid blog by the user i;
calculating the similarity between different users based on the time weight of the users to the paid blogs and the user-paid blogs collection matrix:
Figure QLYQS_30
；
wherein:
Figure QLYQS_31
representing the similarity between user i and user j;
constructing a user similarity matrix S:
Figure QLYQS_32
；
wherein:
the values on the diagonal lines in the user similarity matrix S each represent the user' S own similarity, which is set to 1.
5. The intelligent commodity distribution method according to claim 4, wherein in step S2, the high-order similarity calculation is performed on the user similarity matrix to obtain the user high-order similarity matrix, which includes:
performing high-order similarity calculation on the user similarity matrix to obtain a user high-order similarity matrix, wherein the high-order similarity calculation flow is as follows:
s21: setting the values on the diagonal lines in the user similarity matrix S to 0 to obtain a matrix
Figure QLYQS_33
；
S22: matrix-based
Figure QLYQS_34
Calculating to obtain an adjacent matrix B of the user similarity matrix:
Figure QLYQS_35
；
Figure QLYQS_36
；
wherein:
Figure QLYQS_37
representing a hadamard product operation, representing an element-by-element multiplication operation;
t represents the transpose of the matrix;
s23: initializing a zero matrix of n rows and n columns
Figure QLYQS_38
；
S24: converting the adjacent matrix B into an undirected graph form, wherein the vertex of the undirected graph is the user, and the ith row or the ith column in the adjacent matrix B corresponds to the user i;
s25: vertices for user i and user u
Figure QLYQS_39
Traversing vertices->
Figure QLYQS_40
And any other vertex to obtain a plurality of closed triangle structures, and for any closed triangle structure
Figure QLYQS_41
And +.>
Figure QLYQS_42
Calculating to obtain the closed triangle structure about +.>
Figure QLYQS_43
Higher order similarity of (2):
Figure QLYQS_44
；
wherein:
Figure QLYQS_45
representing a closed triangle structure->
Figure QLYQS_46
In relation to->
Figure QLYQS_47
Higher order similarity of (2);
Figure QLYQS_48
representing vertex +_in undirected graph>
Figure QLYQS_49
The weights of the edges formed;
vertex of the vertex
Figure QLYQS_50
All closed triangular structures with any other vertex
Figure QLYQS_51
The higher-order similarity of the user i and the user u is accumulated, and the accumulated result is the higher-order similarity of the user i and the user u>
Figure QLYQS_52
And will->
Figure QLYQS_53
Update to zero matrix->
Figure QLYQS_54
And zero matrix +.>
Figure QLYQS_55
Ith row and column of (b);
s26: repeating step S25 to obtain high-order similarity between any two users, and updating the initialized zero matrix to obtain a user high-order similarity matrix
Figure QLYQS_56
。
6. The intelligent commodity distribution method according to claim 5, wherein in step S3, a user enhanced similarity matrix is constructed based on the user similarity matrix and the user higher-order similarity matrix, comprising:
based on user similarity matrix S and user high-order similarity matrix
Figure QLYQS_57
Constructing a user enhanced similarity matrix +.>
Figure QLYQS_58
：
Figure QLYQS_59
；
Wherein:
Figure QLYQS_60
the scale threshold is represented and set to 0.8.
7. The intelligent commodity distribution method according to claim 1, wherein said step S4 of determining a user preference objective function indicative of user preferences comprises:
determining a user preference objective function representing user trends
Figure QLYQS_61
：
Figure QLYQS_62
；
Wherein:
Figure QLYQS_63
representing correction parameters, if->
Figure QLYQS_64
If not 0, then->
Figure QLYQS_65
If->
Figure QLYQS_66
0, then->
Figure QLYQS_67
；
Figure QLYQS_68
Representing correction parameters, if->
Figure QLYQS_69
If not 0, then->
Figure QLYQS_70
If->
Figure QLYQS_71
0, then->
Figure QLYQS_72
，/>
Figure QLYQS_73
Representing user enhanced similarity matrix +.>
Figure QLYQS_74
Element values of the ith row and the qth column;
Figure QLYQS_75
representing parameters to be solved optimally, U representing a user feature matrix, V representing a pay blog preference feature matrix,/for>
Figure QLYQS_76
Represents the ith row, < > in the user feature matrix U>
Figure QLYQS_77
Representing the j-th row in the pay blog preference feature matrix V;
Figure QLYQS_78
representing a regularized item parameter, set it to 0.2；
Figure QLYQS_79
Representing the L1 norm.
8. The intelligent commodity distribution method according to claim 7, wherein said step S5 of optimally solving the constructed user preference objective function comprises:
carrying out optimization solution on the constructed user preference objective function to obtain a user feature matrix and a pay blog preference feature matrix, wherein the optimization solution flow is as follows:
s51: order the
Figure QLYQS_80
Representing the solution parameters to be optimized, converting the user preference objective function into +.>
Figure QLYQS_81
And initialize +.>
Figure QLYQS_82
Setting the iteration number of the current algorithm as d, the initial value of d as 0, the maximum iteration number as Max, and initializing +.>
Figure QLYQS_83
Is a unit matrix;
s52: calculation of
Figure QLYQS_84
；
S53: calculating the iteration step length of the (d+1) th iteration
Figure QLYQS_85
：
Figure QLYQS_86
；
Wherein:
Figure QLYQS_87
representing the trace of the calculated matrix;
s54: updating the solution parameters to be optimized based on the iteration step length:
Figure QLYQS_88
；
wherein:
Figure QLYQS_89
representing the identity matrix;
and updating the iteration step length:
Figure QLYQS_90
；
Figure QLYQS_91
；
Figure QLYQS_92
；
if it is
Figure QLYQS_93
Will->
Figure QLYQS_94
As the result of the optimization solution, and extract the user feature matrix +.>
Figure QLYQS_95
Payment blog preference feature matrix +.>
Figure QLYQS_96
；
No order of no
Figure QLYQS_97
The process returns to step S52.
9. A method for intelligent commodity distribution with user data tendency according to claim 8, wherein in step S5, payment blog distribution processing is performed according to user preference of different payment blogs, including:
user characteristic matrix obtained according to optimization solution
Figure QLYQS_98
Payment blog preference feature matrix +.>
Figure QLYQS_99
Calculating preference values of the user i on any paid blog j:
Figure QLYQS_100
；
wherein:
Figure QLYQS_101
representing the preference value of the user i for any paid blog j;
and pushing the paid blogs which are not purchased by the user i and have preference values larger than the preset preference threshold to the user i.
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Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090125397A1 (en) * 2007-10-08 2009-05-14 Imedia Streams, Llc Method and system for integrating rankings of journaled internet content and consumer media preferences for use in marketing profiles
US20110302240A1 (en) * 2010-06-03 2011-12-08 Katsu Saito Content recommendation system, content recommendation device, and content recommendation method
CN105843860A (en) * 2016-03-17 2016-08-10 山东大学 Microblog attention recommendation method based on parallel item-based collaborative filtering algorithm
US20160299992A1 (en) * 2015-04-09 2016-10-13 Yahoo!, Inc. Inductive matrix completion and graph proximity for content item recommendation
CN106776881A (en) * 2016-11-28 2017-05-31 中国科学院软件研究所 A kind of realm information commending system and method based on microblog
US20180032882A1 (en) * 2016-07-27 2018-02-01 Fuji Xerox Co., Ltd. Method and system for generating recommendations based on visual data and associated tags
CN109241203A (en) * 2018-09-27 2019-01-18 天津理工大学 A kind of user preference and distance weighted clustering method of time of fusion factor
CN110110203A (en) * 2018-01-11 2019-08-09 腾讯科技（深圳）有限公司 Resource information method for pushing and server, resource information methods of exhibiting and terminal
CN110263189A (en) * 2019-06-24 2019-09-20 腾讯科技（深圳）有限公司 Recommended method, device, storage medium and the computer equipment of media content
CN111931023A (en) * 2020-07-01 2020-11-13 西北工业大学 Community structure identification method and device based on network embedding
CN113158041A (en) * 2021-04-19 2021-07-23 电子科技大学 Article recommendation method based on multi-attribute features
CN114461899A (en) * 2021-12-24 2022-05-10 新奥新智科技有限公司 Collaborative filtering recommendation method and device for user, electronic equipment and storage medium
CN115357803A (en) * 2021-05-17 2022-11-18 连普乐士株式会社 Account recommendation method, computer device and computer program
CN115545349A (en) * 2022-11-24 2022-12-30 天津师范大学 Time sequence social media popularity prediction method and device based on attribute sensitive interaction
CN116028727A (en) * 2023-03-30 2023-04-28 南京邮电大学 Video recommendation method based on image data processing

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090125397A1 (en) * 2007-10-08 2009-05-14 Imedia Streams, Llc Method and system for integrating rankings of journaled internet content and consumer media preferences for use in marketing profiles
US20110302240A1 (en) * 2010-06-03 2011-12-08 Katsu Saito Content recommendation system, content recommendation device, and content recommendation method
US20160299992A1 (en) * 2015-04-09 2016-10-13 Yahoo!, Inc. Inductive matrix completion and graph proximity for content item recommendation
CN105843860A (en) * 2016-03-17 2016-08-10 山东大学 Microblog attention recommendation method based on parallel item-based collaborative filtering algorithm
US20180032882A1 (en) * 2016-07-27 2018-02-01 Fuji Xerox Co., Ltd. Method and system for generating recommendations based on visual data and associated tags
CN106776881A (en) * 2016-11-28 2017-05-31 中国科学院软件研究所 A kind of realm information commending system and method based on microblog
CN110110203A (en) * 2018-01-11 2019-08-09 腾讯科技（深圳）有限公司 Resource information method for pushing and server, resource information methods of exhibiting and terminal
CN109241203A (en) * 2018-09-27 2019-01-18 天津理工大学 A kind of user preference and distance weighted clustering method of time of fusion factor
CN110263189A (en) * 2019-06-24 2019-09-20 腾讯科技（深圳）有限公司 Recommended method, device, storage medium and the computer equipment of media content
CN111931023A (en) * 2020-07-01 2020-11-13 西北工业大学 Community structure identification method and device based on network embedding
CN113158041A (en) * 2021-04-19 2021-07-23 电子科技大学 Article recommendation method based on multi-attribute features
CN115357803A (en) * 2021-05-17 2022-11-18 连普乐士株式会社 Account recommendation method, computer device and computer program
CN114461899A (en) * 2021-12-24 2022-05-10 新奥新智科技有限公司 Collaborative filtering recommendation method and device for user, electronic equipment and storage medium
CN115545349A (en) * 2022-11-24 2022-12-30 天津师范大学 Time sequence social media popularity prediction method and device based on attribute sensitive interaction
CN116028727A (en) * 2023-03-30 2023-04-28 南京邮电大学 Video recommendation method based on image data processing

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
谢修娟: "情境感知的移动阅读个性化推荐算法研究", 高技术通讯, vol. 29, no. 07, pages 640 - 647 *
魏玲: "融合用户画像与协同过滤的知识付费平台个性化推荐模型", 情报理论与实践, vol. 44, no. 03, pages 188 - 193 *

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