Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (7): 60-65.doi: 10.11896/jsjkx.220900036

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

Event Recommendation Method with Multi-factor Feature Fusion in EBSN

SHAN Xiaohuan, SONG Rui, LI Haihai, SONG Baoyan   

  1. College of Information,Liaoning University,Shenyang 110036,China
  • Received:2022-09-05 Revised:2022-12-24 Online:2023-07-15 Published:2023-07-05
  • About author:SHAN Xiaohuan,born in 1987,Ph.D candidate,is a student member of China Computer Federation.Her main research interests include graph data processing technology and knowledge graph data management,etc.SONG Baoyan,born in 1965,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include database techniques,big data management,etc.
  • Supported by:
    National Key Research and Development Program of China.

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Abstract: Event-based social network(EBSN) is a new kind of complex heterogeneous social network,the personalized event re-commendation in it has certain application value.In recent years,with the rapid development of EBSN,the problem of information overload for event recommendation has been solved by data mining technology.However,it will reduce the accuracy of event re-commendation by only using a single feature attribute or a small number of linear combinations for calculation,and predefining fixed weights.In addition,most approaches ignore the influence of user feedback information on subsequent recommendation.Aiming at the above problems,an event recommendation method fusing multi-factor features is proposed,which consists of two phases.In the query preprocessing phase,the events,historical users and their relationships in EBSN are abstracted as a directed he-terogeneous graph,and the feature information of nodes and edges is extracted for auxiliary storage.A relatively small candidate set is obtained by filtering invalid nodes and edges with the auxiliary data.According to the query context,the query semantics are transformed into the query graphs.In the online query phase,it combines the characteristics of potential friends,event-based collaborative filtering and users’ interests to recommend,and also receives feedback from users on whether they accept the event as a reference factor for subsequent recommendations.Large number of experiments on real datasets and simulated datasets verify the accuracy and user satisfaction of the proposed method in EBSN event recommendation.

Key words: Event-based social network, Multi-factor feature fusion, Event recommendation, Directed heterogeneous graph, Subgraph matching

CLC Number: 

  • TP311
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