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Home > Archive>Volume 29, Issue 10, 2018 >3134-3149. DOI:10.13328/j.cnki.jos.005284
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Jointly Modeling Heterogeneous Social and Content Information for Event Recommendation
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National Key Research & Develop Plan (2016YFB1000702); National Basic Research Program of China (973) (2014CB340402); National Natural Science Foundation of China (61772537, 61772536, 61702522); The Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (15XNLQ06)

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    Abstract:

    Event-Based social networks (EBSNs) have experienced rapid growth in people's daily life. Hence, event recommendation plays an important role in helping people discover interesting online events and attend offline activities face to face in the real world. However, event recommendation is quite different from traditional recommender systems, and there are several challenges:(1) One user can only attend a scarce number of events, leading to a very sparse user-event matrix; (2) The response data of users is implicit feedback; (3) Events have their life cycles, so outdated events should not be recommended to users; (4) A large number of new events which are created every day need to be recommended to users in time. To cope with these challenges, this article proposes to jointly model heterogeneous social and content information for event recommendation. This approach explores both the online and offline social interactions and fuses the content of events to model their joint effect on users' decision-making for events. Extensive experiments are conducted to evaluate the performance of the proposed model on Meetup dataset. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods.

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王绍卿,王征,李翠平,赵衎衎,陈红.联合建模异构社交和内容信息的活动推荐模型.软件学报,2018,29(10):3134-3149

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History
  • Received:November 16,2016
  • Revised:March 16,2017
  • Online: July 20,2017
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