CN107808168A - A kind of social network user behavior prediction method based on strong or weak relation - Google Patents
A kind of social network user behavior prediction method based on strong or weak relation Download PDFInfo
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Abstract
Description
Claims (4)
- A kind of 1. social network user behavior prediction method based on strong or weak relation, it is characterised in that:Comprise the following steps:(1) social networks strong or weak relation is measured:Social network user data are inputted, the pre- of relation power is carried out using Weak Classifier Survey, after Weak Classifier is obtained, using Gradient boosting algorithms, each Weak Classifier is carried out using study sequence Weighting;(2) structure introduces the continuous time hidden Markov model of exogenousd variables:Set SiRepresent the shape of certain sampling time point i user State, AiRepresent the behavior of time point user, FiUser is represented in an ectocine suffered by the i moment, wherein, FiAnd Ai For observable variable, SiFor unobservable state variable;Input the User Status and influence factor t of nearest a period of time1,F1, S1,t2,F2,S2,…,tn,Fn,Sn, ti<ti+1, task is given tn+1>tn, and the S of a period of time recentlyi(i∈[1,n]) And Fi(i ∈ [1, n+1]), export it and correspond to the probability P (S of each staten+1|t1,F1,S1,t2,F2,S2,…,tn,Fn,Sn, tn+1,Fn+1), and ectocine and state are to the probability function P (A of behaviorn+1|tn+1,Sn+1,Fn+1,tn,Fn,tn-1,Fn-1,…, t1,F1,);This mapping is completed using deep neural network;In implementation process, pass through the strong or weak relation obtained in step (1) To FiThe weight that actually enters be adjusted, the training to whole model uses forward, backward algorithm, so as to export user's row For.
- 2. the social network user behavior prediction method according to claim 1 based on strong or weak relation, it is characterised in that:Institute Stating the social network user data of input in step (1) includes the personal information of user, the status information that user delivers, Yong Hudian Praise and comment on.
- 3. the social network user behavior prediction method according to claim 1 based on strong or weak relation, it is characterised in that:Institute Stating Weak Classifier in step (1) includes the relation grader based on user property, based on the grader of interaction statistics, base between user The grader interacted in speech and the grader based on image.
- 4. the social network user behavior prediction method according to claim 1 based on strong or weak relation, it is characterised in that:Institute State user in step (2) state be user inherent state, including it is glad, surprised, sad, angry, frightened and detest emotion.
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Cited By (6)
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CN109165239A (en) * | 2018-07-06 | 2019-01-08 | 重庆邮电大学 | Modeling method is propagated based on the D2D information of user property and contents attribute |
CN109299429A (en) * | 2018-07-11 | 2019-02-01 | 重庆邮电大学 | A kind of dynamic society's relationship modeling method based on Wiener-Hopf equation |
CN110009093A (en) * | 2018-12-07 | 2019-07-12 | 阿里巴巴集团控股有限公司 | For analyzing the nerve network system and method for relational network figure |
CN111414478A (en) * | 2020-03-13 | 2020-07-14 | 北京科技大学 | Social network emotion modeling method based on deep cycle neural network |
WO2021081741A1 (en) * | 2019-10-29 | 2021-05-06 | 深圳大学 | Image classification method and system employing multi-relationship social network |
CN117289940A (en) * | 2023-08-28 | 2023-12-26 | 深圳市众为精密科技有限公司 | Optimization method and system for Zeiss three-dimensional equipment based on off-board programming software |
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Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
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CN109165239A (en) * | 2018-07-06 | 2019-01-08 | 重庆邮电大学 | Modeling method is propagated based on the D2D information of user property and contents attribute |
CN109165239B (en) * | 2018-07-06 | 2021-05-18 | 重庆邮电大学 | D2D information propagation modeling method based on user attributes and content attributes |
CN109299429A (en) * | 2018-07-11 | 2019-02-01 | 重庆邮电大学 | A kind of dynamic society's relationship modeling method based on Wiener-Hopf equation |
CN110009093A (en) * | 2018-12-07 | 2019-07-12 | 阿里巴巴集团控股有限公司 | For analyzing the nerve network system and method for relational network figure |
CN110009093B (en) * | 2018-12-07 | 2020-08-07 | 阿里巴巴集团控股有限公司 | Neural network system and method for analyzing relational network graph |
WO2021081741A1 (en) * | 2019-10-29 | 2021-05-06 | 深圳大学 | Image classification method and system employing multi-relationship social network |
CN111414478A (en) * | 2020-03-13 | 2020-07-14 | 北京科技大学 | Social network emotion modeling method based on deep cycle neural network |
CN111414478B (en) * | 2020-03-13 | 2023-11-17 | 北京科技大学 | Social network emotion modeling method based on deep cyclic neural network |
CN117289940A (en) * | 2023-08-28 | 2023-12-26 | 深圳市众为精密科技有限公司 | Optimization method and system for Zeiss three-dimensional equipment based on off-board programming software |
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