计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (7): 60-65.doi: 10.11896/jsjkx.220900036

• 数据库&大数据&数据科学 • 上一篇    下一篇

多因素特征融合的EBSN活动推荐方法

单晓欢, 宋瑞, 李海海, 宋宝燕   

  1. 辽宁大学信息学院 沈阳 110036
  • 收稿日期:2022-09-05 修回日期:2022-12-24 出版日期:2023-07-15 发布日期:2023-07-05
  • 通讯作者: 宋宝燕(bysong@lnu.edu.cn)
  • 作者简介:(shanxiaohuan@lnu.edu.cn)
  • 基金资助:
    国家重点研发计划

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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摘要： 基于活动的社交网络(Event-based Social Network,EBSN)是一种新型的复杂异构社交网络,其中的个性化活动推荐具有一定的应用价值。近年来,随着EBSN的快速发展,传统方法利用数据挖掘技术有效解决了活动推荐的信息过载问题。然而,仅利用单特征属性或少量线性组合进行计算,且预定义固定权重将降低活动推荐的准确度,此外大多数方法忽略了用户反馈信息对后续推荐的影响。针对上述问题,提出了一种两阶段构成的多因素特征融合的活动推荐方法。查询预处理阶段,将EBSN中的活动、历史用户及其之间的关系抽象为有向异构图,并提取节点及边的特征信息进行辅助存储;利用该辅助数据过滤无效节点及边,进而获得相对较小的候选集;根据查询语境,将查询语义转化为查询图。在线查询阶段,融合潜在好友关系、基于活动的协同过滤以及用户对活动的兴趣这3方面特征进行活动推荐,并接收用户是否接受活动的反馈信息作为后续推荐的参考因素。在真实数据集和模拟数据集上进行了大量实验,结果表明所提方法相比对比算法在EBSN中活动推荐的精确度和用户的满意度方面更优。

关键词: 基于活动的社交网络, 多因素特征融合, 活动推荐, 有向异构图, 子图匹配

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

中图分类号: 

  • TP311
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