Computer Science ›› 2020, Vol. 47 ›› Issue (4): 50-53.doi: 10.11896/jsjkx.190700175

• Database & Big Data & Data Science • Previous Articles     Next Articles

Personalized Recommendation Algorithm Based on User Preference Feature Mining

LIU Xiao-fei, ZHU Fei, FU Yu-chen, LIU Quan   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2019-07-25 Online:2020-04-15 Published:2020-04-15
  • Contact: FU Yu-chen,born in 1968,Ph.D,professor,is a member of China Computer Federation.His main research interests include reinforcement learning and intelligence information processing.
  • About author:LIU Xiao-fei,born in 1995,postgra-duate.His main research interests include machine learning and intelligence information processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (60673092).

Abstract: For the purpose of personalized recommendation ability of social network,this paper proposed a personalized recommendation algorithm based on user behavior feature mining according to the distribution of user behavior.The user behavior information feature mining model of social network is constructed,the big data fusion scheduling method is used to fuse the behavior information of social network user characteristics,and the semantic information features that reflect the user preference are extracted.According to the user’s behavior feature groups from the aspects of emotion,keywords and structure,combined with the fuzzy information perception method,the information scheduling in the process of personalized recommendation of social network is carried out.Under the control of association rules constraints,a hybrid recommendation model of user preference features is constructed to realize user preference feature mining,and personalized information recommendation of social networks is realized according to semantic distribution and user behavior preference.The simulation results show that the poposed method has good feature resolution ability and accurate recognition ability to user behavior features,which improves the confidence level of social network recommendation output.

Key words: Feature mining, Personalized recommendation, Social network, User preference

CLC Number: 

  • TP391
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