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| 联合建模异构社交和内容信息的活动推荐模型 |
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王绍卿1,2,3, 王征1,2, 李翠平1,2, 赵衎衎1,2, 陈红1,2
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1.数据工程与知识工程教育部重点实验室(中国人民大学), 北京 100872;2.中国人民大学 信息学院, 北京 100872;3.山东理工大学 计算机科学与技术学院, 山东 淄博 255091
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| 摘要: |
| 随着基于活动的社交网络的迅速发展，活动推荐已成为一个重要的工具，帮助人们在线上发现有趣的活动，并在线下面对面地参与活动.但是，相对于传统的推荐系统，活动推荐面临着很多挑战.（1）用户只能参与很少的活动，这就导致一个非常稀疏的用户-活动矩阵；（2）用户对活动的响应是隐性反馈；（3）活动本身有生命周期，已经过期的活动不能再向用户推荐；（4）每天会有很多新的活动产生，需要及时向用户推荐.为了应对这些挑战，提出一个联合建模异构社交和内容信息的活动推荐模型.该模型可同时探索用户的线上和线下社交活动，并结合活动内容建模用户对活动的决策行为.在Meetup数据集上做实验以评估所提出模型的性能.实验结果表明，提出的模型优于其他方法. |
| 关键词: 基于活动的社交网络 社交网络分析 活动推荐 联合建模 泊松因子分解 |
| DOI：10.13328/j.cnki.jos.005284 |
| 分类号: |
| 基金项目:国家重点研发计划（2016YFB1000702）；国家基础研究发展计划（973）（2014CB340402）；国家自然科学基金（61772537，61772536，61702522）；中国人民大学科学研究基金（中央高校基本科研业务费专项资金资助）（15XNLQ06） |
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| Jointly Modeling Heterogeneous Social and Content Information for Event Recommendation |
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WANG Shao-Qing1,2,3, WANG Zheng1,2, LI Cui-Ping1,2, ZHAO Kan-Kan1,2, CHEN Hong1,2
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1.Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education(Renmin University of China), Beijing 100872, China;2.Information School, Renmin University of China, Beijing 100872, China;3.School of Computer Science and Technology, Shandong University of Technology, Zibo 255091, China
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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sparse user-event matrix; (2) The response data of users is implicit feedback; (3) Events have their life cycles, so outdated events should not be recommended to users; (4) A large number of new events which are created every day need to be recommended to users in time.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. |
| Key words: event based social network social network analysis event recommendation jointly modeling Poisson factorization |
