活动社交网络(EBSNs)为用户提供了方便的组织、参加和分享社交活动的平台。该文面向EBSNs活动推荐问题,提出了包含活动(Event)、主办方(Sponsor)和用户(User)的ESU图模型,深入揭示了EBSNs的实体及其社交关系。因为用户参加活动受多个因素影响,我们提出了基于ESU图的活动推荐多因素决策模型,包括社交影响力、活动内容、活动地点及活动时间。根据ESU图特点,提出了基于双向重启随机游走算法BD-RWR的实体重要度计算方法。选取真实的EBSNs平台—豆瓣同城验证所提方法的有效性。实验结果表明,该文提出的ESU图模型及融合了多因素的活动推荐模型,与已有最新方法相比,有效地提升了用户参加活动的推荐效果。
Abstract
Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Focusing on recommending upcoming events for users in EBSNs, we present a hybrid social model of Event-Sponsor-User graph by incorporating events, event sponsors (event groups) and users, to best capture the entities and their complex social relations in EBSNs. Due to the fact that users' interests are motivated by a complex set of factors, we propose a model of event recommendation with multiple factors based on ESU graph, which includes social influence, event content, location and time. According to the characteristics of ESU graph, the calculation of entity importance is estimated by bidirectional random walk with restart (BD-RWR). A comprehensive performance evaluation on real-world data sets collected from DoubanEvent shows the proposed method is more effective than state-of-the-art methods.
关键词
活动社交网络 /
活动推荐 /
图模型 /
多因素推荐模型 /
双向重启随机游走算法
{{custom_keyword}} /
Key words
event-based social network /
event recommendation /
graph model /
with multiple factors recommendation model /
bidirectional random walk with restart
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Liu X,He Q,Tian Y,et al.Event-based social networks:linking the online and offline social worlds[C]//Proceedings of the 18th ACM SIGKDD conference on Knowledge Discovery and Data Mining,2012:1032-1040.
[2] Chin A,Tian J,Han J,et al.A study of offline events and its influence on online social connections in Douban[C]//Proceedings of the GreenCom and iThings/CPSCom,2013:1021-1028.
[3] Xu T,Zhong H,Zhu H,et al.Exploring the impact of dynamic mutual influence on social event participation[C]//Proceedings of the 2nd International Conference on Sustainable Design and Manufacturing,2015:262-270.
[4] Jamali M,Ester M.A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings the fourth ACM conference on Recommender systems,2010:135-142.
[5] Li K,Lu W,Bhagat S,et al.On social event organization[C]//Proceedings of the 19th ACM SIGKDD conference on Knowledge Discovery and Data Mining,2014:1206-1215.
[6] Yu Z,Du R,Guo B,et al.Who should I invite for my party? combining user preference and influence maximization for social events[C]//Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing,2015:879-883.
[7] Du R,Yu Z,Mei T,et al.Predicting activity attendance in event-based social networks:content,context and social influence[C]//Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing,2014:425-434.
[8] Zhang X M,Zhao J,Cao G H.Who will attend? - predicting event attendance in event-based social network[C]//Proceedings of the 16th IEEE International Conference on Mobile Data Management (MDM),Pittsburgh,PA,USA,2015:74-83.
[9] Li X,Cheng X,Su S,et al.A hybrid collaborative filtering model for social influence prediction in event-based social networks[J].Neurocomputing.2017,230:197-209.
[10] Qiao Z,Zhang P,Cao Y,et al.Combining heterogenous social and geographical information for event recommendation[C]//Proceedings of the 28th AAAI,2014:145-151.
[11] Zhang W,Wang J.A collective bayesian poisson factorization model for cold-start local event recommendation[C]//Proceedings of the 21th ACM SIGKDD conference on Knowledge Discovery and Data Mining,2015:1455-1464.
[12] Liu X,Aberer K.SoCo:a social network aided context-aware recommender system[C]//Proceedings of the 22nd international conference on World Wide Web,2013:781-802.
[13] Tong Y X,She J Y,Meng R.Bottleneck-aware arrangement over event-based social networks:the max-min approach[J].World Wide Web,2016,19(6):1151-1177.
[14] Müngen A A,Kaya M.A novel method for event recommendation in Meetup[C]//Proceedings the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,2017:959-965.
[15] Liu S H,Wang B,Xu,M H.Event recommendation based on graph random walking and history preference reranking[C]//Proceedings of the SIGIR17,Shinjuku,Tokyo,Japan,2017:861-864.
[16] Tong H H,Faloutsos C,Pan J Y.Fast random walk with restart and its applications[C]//Proceedings of the 6th IEEE International Conference on Data Mining.Hong Kong,China:IEEE Press,2006:613-622.
[17] Yang D,Zhang D,Yu Z,et al.Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs[C]//Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing,2013:479-488.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金(61403156);江苏省第五期333高层次人才培养工程;江苏高校品牌专业建设工程资助项目(PPZY2015A038);连云港521高层次人才培养对象资助项目
{{custom_fund}}
PDF(2200 KB)
