摘要:基于事件的社交网络使得事件推荐受到越来越多的关注.不同于其他推荐问题（如电影推荐等），事件推荐具有3类不同信息：用户构成的异构社交网络关系信息（在线社交网络和离线社交网络）、用户/事件的内容信息、用户对事件的隐式反馈信息.如何有效融合这些信息进行事件推荐是该领域****普遍关注的问题.提出一种新的混合事件推荐方法CHS-BPR，该方法以贝叶斯潜在因子模型为基本框架来处理用户对事件的隐式反馈信息，同时考虑用户/事件的内容信息和用户之间的异构社交网络信息，首次实现了同时使用3种信息来做事件推荐，并以真实数据集验证了所提方法的有效性.



Abstract:The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users' ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem. Different from classic recommendation problems (e.g. movies), event recommendation generally faces three complex problems:Heterogeneous social relationships (online and offline) among users, the implicit feedback data and the content-context information of users/events. How to effectively fuse this information for event recommendation is a common concern for scholars in this field. This work presents a Bayesian latent factor model that combines users/items content-context information and heterogeneous social information for event recommendation. Experimental results on several real-world datasets demonstrate the proposed method can efficiently tackle with implicit feedback characteristic for event recommendation.



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