CN107609786B - Method for constructing user behavior preference change model under online social network - Google Patents

Method for constructing user behavior preference change model under online social network Download PDF

Info

Publication number
CN107609786B
CN107609786B CN201710883112.XA CN201710883112A CN107609786B CN 107609786 B CN107609786 B CN 107609786B CN 201710883112 A CN201710883112 A CN 201710883112A CN 107609786 B CN107609786 B CN 107609786B
Authority
CN
China
Prior art keywords
user
interest
subtopic
topic
user interest
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201710883112.XA
Other languages
Chinese (zh)
Other versions
CN107609786A (en
Inventor
汪材印
崔琳
梁楠楠
谈成访
潘正高
彭智超
戚溪溪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou University
Original Assignee
Suzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou University filed Critical Suzhou University
Priority to CN201710883112.XA priority Critical patent/CN107609786B/en
Publication of CN107609786A publication Critical patent/CN107609786A/en
Application granted granted Critical
Publication of CN107609786B publication Critical patent/CN107609786B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention discloses a method for constructing a user behavior preference change model under an online social network. The method comprises the following steps: under the set Hadoop parallel distributed processing environment, after interest topic information of a user is processed through a Map-Reduce process, calculating the drift probability of the interest topic of the user by adopting symmetrical KL divergence, and determining an interest change point of the user; mining long-term interest and short-term interest of a user; mining the weight of each interest; and determining a user behavior preference change model according to the long-term interest, the short-term interest and the weight of each interest of the user. The invention researches the behavior preference change of the user in the online social network, knows the personalized preference of the user in different time and space situations, and finds information resources which are really interested by the user from the vast online social network environment according to the current situation information and the interest of the user, thereby meeting the personalized demand of the user on the information and practically improving the user experience.

Description

Method for constructing user behavior preference change model under online social network
Technical Field
The invention relates to the field of online social networks, in particular to a method for constructing a user behavior preference change model in an online social network.
Background
User behavior preference change modeling (namely user interest change modeling) refers to induction of related interests and behavior information of a user, a user model capable of describing behavior change characteristics of the user is deduced based on the induction, the user model is one of key technologies of a recommendation system, an efficient recommendation algorithm can be designed only by accurately grasping user interest changes, and therefore user experience is improved.
At present, although the problem of personalized recommendation under an online social network is studied to a certain extent, the behavior preference of a user under the online social network often changes along with time, while the traditional modeling method mostly adopts a static research method and does not consider the problem of the behavior preference change of the user under the online social network.
In summary, in the prior art, user interest modeling mostly adopts a static research method, and the problem that the online social network downlink is preference change is not considered.
Disclosure of Invention
The embodiment of the invention provides a method for constructing a user behavior preference change model in an online social network, which is used for solving the problem that the change of the user behavior preference is not considered by adopting a static research method in the prior art.
The embodiment of the invention provides a method for constructing a user behavior preference change model under an online social network, which comprises the following steps:
under the established Hadoop parallel distributed processing environment, after interest topic information of a user is processed through a Map-Reduce process, the drift probability of the interest topic of the user is calculated by adopting symmetrical KL divergence, and a user interest change point is determined;
dividing the evolution of the drift trajectory of the user interest subtopic into three types of new interest generation, interest maintenance and interest disappearance according to the relationship between the user interest subtopic and the forward associated subtopic and the backward associated subtopic; when the user behavior preference changes, analyzing the corresponding user interest change points, and mining the long-term interest and the short-term interest of the user;
calculating the absolute intensity and the relative intensity of the user behavior preference in the whole life cycle, constructing the intensity trend of the change track of the user behavior preference, and mining the weight of each interest;
and determining a user behavior preference change model according to the long-term interest, the short-term interest and the weight of each interest of the user.
Preferably, the calculating the drift probability of the user interest topic by using the symmetrical KL divergence and determining the user interest change point includes:
the user interest is expressed by using the occurrence probability of the feature words in the user scene log, and the semantic similarity between the user interest topics in different periods is judged to correspond to the approximate degree of measurement between two probability distributions; let the sliding window contain N time slices, and let the interest j of the user in time slice t be recorded as
Figure BDA0001419582690000021
The original KL divergence is asymmetrical, but the semantic similarity between the user interest topics in different periods is symmetrical, namely, for any user interest topic
Figure BDA0001419582690000022
And
Figure BDA0001419582690000023
Figure BDA0001419582690000024
and
Figure BDA0001419582690000025
similarity of
Figure BDA0001419582690000026
And
Figure BDA0001419582690000027
the similarity of the KL is equal, the original KL divergence is improved, and the similarity of the user interest subtopic based on the symmetrical KL divergence is determined as follows:
Figure BDA0001419582690000028
wherein p (w) and q (w) respectively represent the characteristic words w in the user interest sub-topic
Figure BDA0001419582690000029
And
Figure BDA00014195826900000210
v represents a collection of vocabulary dictionaries.
Preferably, the first and second liquid crystal films are made of a polymer,
the forward association sub-topic is: and in each time slice i (i ═ t-N, …, t-1) in the sliding window
Figure BDA0001419582690000031
The user interest subtopic with the greatest similarity is recorded as
Figure BDA0001419582690000032
The backward correlation sub-topic is: and in each time slice i (i ═ t +1, …, t + N) in the sliding window
Figure BDA0001419582690000033
The user interest subtopic with the greatest similarity is recorded as
Figure BDA0001419582690000034
Preferably, the first and second liquid crystal films are made of a polymer,
the new interest generation comprises the following steps: for user interest subtopic
Figure BDA0001419582690000035
If there is no forward related interest subtopic
Figure BDA0001419582690000036
So that
Figure BDA0001419582690000037
And
Figure BDA0001419582690000038
is greater than a threshold value epsilon and does not satisfy
Figure BDA0001419582690000039
Then
Figure BDA00014195826900000310
Is an emerging interesting topic generated in time slice t;
the interest preservation includes: for user interest subtopic
Figure BDA00014195826900000311
If there is a forward associated interest subtopic
Figure BDA00014195826900000312
So that
Figure BDA00014195826900000313
And
Figure BDA00014195826900000314
is greater than a threshold value epsilon, i.e.
Figure BDA00014195826900000315
And is
Figure BDA00014195826900000316
Is also that
Figure BDA00014195826900000317
Backward related interest sub-topic of (1), i.e.
Figure BDA00014195826900000318
Then
Figure BDA00014195826900000319
Is that
Figure BDA00014195826900000320
The interest of the user does not change much;
the interest disappears, including: for user interest subtopic
Figure BDA00014195826900000321
If no backward correlation subtopic exists
Figure BDA00014195826900000322
So that
Figure BDA00014195826900000323
And
Figure BDA00014195826900000324
is greater than a threshold value epsilon and does not satisfy
Figure BDA00014195826900000325
Then
Figure BDA00014195826900000326
Is died in time slice t, the user no longer has this interest.
Preferably, the first and second liquid crystal films are made of a polymer,
the absolute intensities include: let di＝{di1…diMRepresents a mobile user context log diM denotes a user scene log diThe number of words contained, i, represents the user context log diThe expressed user interest topic; the absolute intensity of the user interest topic i at time t takes the following formula:
Figure BDA00014195826900000327
the relative intensities include: the relative strength of the user interest topic i at time t takes the following formula:
Figure BDA0001419582690000041
wherein t' is any time slice between t-N and t-1, K is the number of the user interest topics, and p is any one of the user interest topics K.
In the embodiment of the invention, a method for constructing a user behavior preference change model under an online social network is provided, and compared with the prior art, the method has the following beneficial effects: according to the invention, under the established Hadoop parallel distributed processing environment, the behavior preference change of the user is researched, the personalized preference of the user at different time is known, and according to the current situation information and the interest of the user, information resources which are really interested by the user are found from the vast online social network environment, the personalized requirement of the user on the information is met, and the user experience is practically improved; the research of the invention has important value for improving the application effect of the personalized service and seeking the breakthrough of the personalized information service technology under the online social network, so as to promote the further development of the online social network application and service to intellectualization.
Drawings
FIG. 1 is a flowchart of a method for constructing a user behavior preference change model in an online social network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the generation of a user interest sub-topic according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating inheritance of a user interest sub-topic provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of user interest sub-topic extinction provided in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for constructing a user behavior preference change model in an online social network according to an embodiment of the present invention. As shown in fig. 1, the method includes:
and S101, under the set Hadoop parallel distributed processing environment, calculating the drift probability of the user interest topic by adopting symmetrical KL divergence, and determining the user interest change point.
In a Hadoop cluster environment, the pre-processing of user interest is divided into a Map process and a Reduce process. In the Map process, when a task is executed, input data of each Map is derived from one divided block Split (each data block size is set to 64MB) in the HDFS. After the Map process processing, the Map outputs a key-value pair, and sets different interest topics T corresponding to the user by the key, wherein the value is the probability value of the topic corresponding to the key. Subsequently, in the Shuffle stage, sequencing and merging operations are executed on the processing result of the Map process, and then the key-value information processed by Shuffle is input to the Reduce process for processing; and when the Reduce task is executed, dividing the Reduce task into 5 Reduce tasks according to the sequence of 26 English letters, secondarily merging the result data sets according to the sequence of key value initials, and outputting the final processing result of the Map-Reduce operation so as to further analyze how the behavior preference of the user changes along with time and the situation.
In the online social network, although the behavior preference of the user changes with time and the situation, there is a certain correlation between the interest topics of the users in different periods, and the correlation is reflected as semantic similarity between the interest topics of the users in different periods. Therefore, the user interest is expressed by using the occurrence probability of the feature words, and the semantic similarity between the user interest topics at different periods is judged to correspond to the similarity degree between two probability distributions. Let the sliding window contain N time slices, and let the interest j of the user in time slice t be recorded as
Figure BDA0001419582690000051
The original KL divergence is asymmetrical, but the semantic similarity between the user interest topics in different periods is symmetrical, namely, for any user interest topic
Figure BDA0001419582690000052
And
Figure BDA0001419582690000053
Figure BDA0001419582690000054
and
Figure BDA0001419582690000055
similarity of
Figure BDA0001419582690000056
And
Figure BDA0001419582690000057
the similarity of the KL is equal, the original KL divergence is improved, and the similarity of the user interest subtopic based on the symmetrical KL divergence is preliminarily conceived as follows:
Figure BDA0001419582690000061
wherein p (w) and q (w) respectively represent the characteristic words w in the user interest sub-topic
Figure BDA0001419582690000062
And
Figure BDA0001419582690000063
the probability of occurrence of.
And S102, dividing the evolution of the drift trajectory of the user interest subtopic into three types of new interest generation, interest maintenance and interest disappearance according to the relationship between the user interest subtopic and the forward associated subtopic and the backward associated subtopic, analyzing corresponding user interest change points when the user behavior preference changes, and mining the long-term interest and the short-term interest of the user.
The user interest topic has a certain life cycle and is composed of a group of user interest subtopics which are mutually related. Let N be the size of the time sliding window, for the user interest sub-topic in time slice t
Figure BDA0001419582690000064
There is an association between user interest sub-topics within the sliding window adjacent to the time slice t. And in each time slice i (i ═ t-N, …, t-1) in the sliding window
Figure BDA0001419582690000065
The user interest sub-topic with the largest similarity is called
Figure BDA0001419582690000066
Is referred to as a forward association subtopic
Figure BDA0001419582690000067
And in time slice i (i ═ t +1, …, t + N)
Figure BDA0001419582690000068
The sub-topic with the greatest similarity is called
Figure BDA0001419582690000069
Is written as a backward association subtopic
Figure BDA00014195826900000610
According to the relationship between the user interest subtopic and the forward and backward related subtopics thereof, the evolution of the drift trajectory of the user interest subtopic is divided into three types of emerging interest generation, interest maintenance and interest disappearance for research, which are respectively introduced as follows:
user interest subtopic generation-New interest Generation
For user interest subtopic
Figure BDA00014195826900000611
If there is no forward related interest subtopic
Figure BDA00014195826900000612
So that
Figure BDA00014195826900000613
And
Figure BDA00014195826900000614
is greater than a threshold value epsilon and does not satisfy
Figure BDA00014195826900000615
Then
Figure BDA00014195826900000616
Is an emerging interesting theme generated in time slice t, as shown in fig. 2.
User interest subtopic inheritance-interest preservation
For user interest subtopic
Figure BDA00014195826900000617
If there is a forward associated interest subtopic
Figure BDA00014195826900000618
So that
Figure BDA00014195826900000619
And
Figure BDA00014195826900000620
is greater than a threshold value epsilon, i.e.
Figure BDA00014195826900000621
And is
Figure BDA00014195826900000622
Is also that
Figure BDA00014195826900000623
Backward related interest sub-topic of (1), i.e.
Figure BDA0001419582690000071
Then
Figure BDA0001419582690000072
Is that
Figure BDA0001419582690000073
As shown in fig. 3. This phenomenon indicates that the interests of the user do not change much.
User interest subtopic disappearance-interest disappearance
For user interest subtopic
Figure BDA0001419582690000074
If no backward correlation subtopic exists
Figure BDA0001419582690000075
So that
Figure BDA0001419582690000076
And
Figure BDA0001419582690000077
is greater than a threshold value epsilon and does not satisfy
Figure BDA0001419582690000078
Then
Figure BDA0001419582690000079
Is died out in time slice t, as shown in fig. 4, indicating that the user no longer has this interest.
Step S103, calculating the absolute intensity and the relative intensity of the user behavior preference in the whole life cycle, constructing the intensity trend of the change track of the user behavior preference, and mining the weight of each interest.
And step S104, determining a user behavior preference change model according to the long-term interest, the short-term interest and the weight of each interest of the user.
With the development of time, the user interest topic strength changes along with the change of the user behavior preference. The invention excavates the weight of each interest by calculating the absolute intensity and the relative intensity of each interest of the user so as to judge whether the degree of interest of the user changes.
Let di＝{di1…diMRepresents a mobile user context log diM denotes a user scene log diThe number of words contained, i, represents the user context log diThe expressed user interest topic. The absolute intensity of the user interest topic i at the time t is calculated by the following method:
Figure BDA00014195826900000710
within the time slice of t', when the word | dij|t′Delta (| d) when belonging to the user interest topic iij|t′I) 1, otherwise δ (| d)ij|t′,i)＝0。
The relative strength of the user interest topic i at the time t is calculated by the following method:
Figure BDA00014195826900000711
wherein t' is any time slice between t-N and t-1, K is the number of the user interest topics, and p is any one of the user interest topics K. Therefore, the intensity change of each behavior preference of the user can be realized by iteratively calculating the absolute intensity and the relative intensity of the user interest topic i in the whole topic life cycle, so as to judge whether the interest degree of each interest changes.
Based on the analysis, a user behavior preference change model can be constructed on the basis of mining the long-term interest and the short-term interest of the user and the weight of each interest.
In conclusion, the invention researches the behavior preference change of the user in the online social network of the user, learns the personalized preference of the user under different time-space situations, and discovers information resources which are really interested by the user from the vast social network environment according to the situation information at the current moment and the interest of the user, thereby meeting the personalized demand of the user on the information and practically improving the user experience; the research of the invention has important value for improving the application effect of the personalized service and seeking the breakthrough of the personalized information service technology in the online social network, so as to promote the further development of the online social application and service towards intellectualization.
The above disclosure is only a few specific embodiments of the present invention, and those skilled in the art can make various modifications and variations of the present invention without departing from the spirit and scope of the present invention, and it is intended that the present invention encompass these modifications and variations as well as others within the scope of the appended claims and their equivalents.

Claims (2)

1. A method for constructing a user behavior preference change model under an online social network is characterized by comprising the following steps:
under the established Hadoop parallel distributed processing environment, after interest topic information of a user is processed through a Map-Reduce process, the drift probability of the interest topic of the user is calculated by adopting symmetrical KL divergence, and a user interest change point is determined;
according to the relationship between the user interest subtopic and the forward related subtopic and the backward related subtopic, the evolution of the drift track of the user interest subtopic is divided into three types of new interest generation, interest maintenance and interest disappearance, when the user behavior preference changes, the corresponding user interest change point is analyzed, and the long-term interest and the short-term interest of the user are mined;
the forward associated user interest subtopic is: neutralizing each time slice t' in the sliding window
Figure FDA0002834521530000011
The user interest subtopic with the greatest similarity is recorded as
Figure FDA0002834521530000012
Wherein t' is t-N, …, t-1, N is an integer;
the backward associated user interest subtopics are: neutralizing each time slice t' in the sliding window
Figure FDA0002834521530000013
The user interest subtopic with the greatest similarity is recorded as
Figure FDA0002834521530000014
The new interest generation comprises the following steps: for user interest subtopic
Figure FDA0002834521530000015
If there is no forward related interest sub-topic Tl mSo that
Figure FDA0002834521530000016
And Tl mIs greater than a threshold epsilon, i.e., does not satisfy the user interest subtopic similarity based on the symmetric KL divergence
Figure FDA0002834521530000017
Then
Figure FDA0002834521530000018
Is an emerging interesting topic generated in time slice t;
the interest preservation includes: for user interest subtopic
Figure FDA0002834521530000019
If there is a forward associated interest subtopic
Figure FDA00028345215300000110
So that
Figure FDA00028345215300000111
And Tl mIs greater than a threshold value epsilon, i.e.
Figure FDA00028345215300000112
And is
Figure FDA00028345215300000113
Is also Tl mBackward related interest sub-topic of (1), i.e.
Figure FDA00028345215300000114
Then
Figure FDA00028345215300000115
Is Tl mThe interest of the user does not change much;
the interest disappears, including:for user interest subtopic
Figure FDA00028345215300000116
If no backward correlation subtopic T existsl mSo that
Figure FDA00028345215300000117
And Tl mIs greater than a threshold value epsilon and does not satisfy
Figure FDA00028345215300000118
Then the subject
Figure FDA00028345215300000119
Dying in time slice t, the user no longer has this interest;
calculating the absolute intensity and the relative intensity of the user behavior preference in the whole life cycle, constructing the intensity trend of the change track of the user behavior preference, and mining the weight of each interest;
the absolute intensities include: let di＝{di1…diMRepresents a mobile user context log diM denotes a user scene log diThe number of words contained, i, represents the user context log diThe expressed user interest topic; the absolute intensity of the user interest topic i in the time slice t adopts the following formula:
Figure FDA0002834521530000021
the relative intensities include: the relative strength of the user interest topic i in the time slice t adopts the following formula:
Figure FDA0002834521530000022
wherein t' is any time slice between t-N and t-1, K is the number of the user interest topics, and p is any one of the user interest topics K;
and determining a user behavior preference change model according to the long-term interest, the short-term interest and the weight of each interest of the user.
2. The method for constructing a user behavior preference change model under an online social network according to claim 1, wherein the calculating the drift probability of the user interest topic by using the symmetrical KL divergence and determining the user interest change point comprises:
the user interest is expressed by using the occurrence probability of the feature words in the user scene log, and the semantic similarity between the user interest topics in different periods is judged to correspond to the approximate degree of measurement between two probability distributions; let the sliding window contain N time slices, and let the interest j of the user in time slice t be recorded as
Figure FDA0002834521530000023
The original KL divergence is asymmetrical, but the semantic similarity between the user interest topics in different periods is symmetrical, namely, for any user interest topic
Figure FDA0002834521530000024
And Tl m，
Figure FDA0002834521530000025
And Tl mSimilarity to Tl mAnd
Figure FDA0002834521530000026
the similarity of the KL is equal, the original KL divergence is improved, and the similarity of the user interest subtopic based on the symmetrical KL divergence is determined as follows:
Figure FDA0002834521530000031
wherein p (w) and q (w) respectively represent the characteristic vocabulary w in the user interest sub-mainQuestion (I)
Figure FDA0002834521530000032
And Tl mV represents a collection of vocabulary dictionaries.
CN201710883112.XA 2017-09-26 2017-09-26 Method for constructing user behavior preference change model under online social network Active CN107609786B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710883112.XA CN107609786B (en) 2017-09-26 2017-09-26 Method for constructing user behavior preference change model under online social network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710883112.XA CN107609786B (en) 2017-09-26 2017-09-26 Method for constructing user behavior preference change model under online social network

Publications (2)

Publication Number Publication Date
CN107609786A CN107609786A (en) 2018-01-19
CN107609786B true CN107609786B (en) 2021-02-09

Family

ID=61058575

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710883112.XA Active CN107609786B (en) 2017-09-26 2017-09-26 Method for constructing user behavior preference change model under online social network

Country Status (1)

Country Link
CN (1) CN107609786B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111310033B (en) * 2020-01-23 2023-05-30 山西大学 Recommendation method and recommendation device based on user interest drift

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101082972A (en) * 2007-05-30 2007-12-05 华为技术有限公司 Method and device for forecasting user's interest to commercial product and method for publishing advertisement thereof
CN106897363A (en) * 2017-01-11 2017-06-27 同济大学 The text for moving tracking based on eye recommends method
CN107193456A (en) * 2017-05-08 2017-09-22 上海交通大学 Commending system and method based on slidingtype interactive operation
US10430480B2 (en) * 2012-08-22 2019-10-01 Bitvore Corp. Enterprise data processing

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101082972A (en) * 2007-05-30 2007-12-05 华为技术有限公司 Method and device for forecasting user's interest to commercial product and method for publishing advertisement thereof
US10430480B2 (en) * 2012-08-22 2019-10-01 Bitvore Corp. Enterprise data processing
CN106897363A (en) * 2017-01-11 2017-06-27 同济大学 The text for moving tracking based on eye recommends method
CN107193456A (en) * 2017-05-08 2017-09-22 上海交通大学 Commending system and method based on slidingtype interactive operation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
混合模型的用户兴趣漂移算法;郭新明等;《智能系统学报》;20100430;第5卷(第2期);第181-184页 *

Also Published As

Publication number Publication date
CN107609786A (en) 2018-01-19

Similar Documents

Publication Publication Date Title
CN107562947B (en) Method for establishing dynamic instant recommendation service model under mobile space-time perception
Nguyen et al. Real-time event detection for online behavioral analysis of big social data
WO2022041979A1 (en) Information recommendation model training method and related device
CN107463704B (en) Search method and device based on artificial intelligence
US20230229863A1 (en) Content editing using AI-based content modeling
US20160170982A1 (en) Method and System for Joint Representations of Related Concepts
Deng et al. A user identification algorithm based on user behavior analysis in social networks
CN108108743B (en) Abnormal user identification method and device for identifying abnormal user
CN111079442A (en) Vectorization representation method and device of document and computer equipment
WO2023124029A1 (en) Deep learning model training method and apparatus, and content recommendation method and apparatus
CN105760499A (en) Method for analyzing and predicting online public opinion based on LDA topic models
WO2017087833A1 (en) Measuring influence propagation within networks
Díaz-Morales Cross-device tracking: Matching devices and cookies
Kim et al. SMS spam filterinig using keyword frequency ratio
Zhao et al. Text sentiment analysis algorithm optimization and platform development in social network
US20230401382A1 (en) Dynamic Language Models for Continuously Evolving Content
MacDermott et al. Using deep learning to detect social media ‘trolls’
CN107609786B (en) Method for constructing user behavior preference change model under online social network
Zhu et al. MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network.
Dasondi et al. An implementation of graph based text classification technique for social media
CN112307738A (en) Method and device for processing text
CN114424197A (en) Rare topic detection using hierarchical clustering
Bauersfeld et al. Cracking double-blind review: Authorship attribution with deep learning
Tang Some new confidence intervals for Kaplan‐Meier based estimators from one and two sample survival data
US10778803B2 (en) Sub-social network based on contextual inferencing

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant