基于活动的社交网络中的群组推荐算法设计

在基于活动的社交网络（EBSN）中，群组中聚集了具有相似兴趣的用户，并为用户组织并举办线下活动，在社区的发展中起到了至关重要的作用，因而理解用户加入群组的原因和群组形成的过程在社交网络的研究中是一个重要的议题.本文通过基于活动的社交网络中的一些相关内容信息，比如社交网络中的标签信息和地理位置信息，来辅助推荐系统更好地为用户预测对于群组的偏好.本文提出了SEGELER （pair-wiSE Geo-social Event-based LatEnt factoR）模型，并使用这些社交网络中的信息，来为用户的兴趣进行预测.通过在真实的EBSN数据集上进行实验与验证，本文的模型不仅可以有效提升对于用户偏好的预测，也可以缓解冷启动问题.

Predicting User Preferences for Groups in Event-Based Social Networks

In event-based social networks (EBSN), groups that aggregate users with similar interests for sharing events play important roles in community development. Understanding why people join a group and how groups are formed is particularly an interesting issue in social science. In this paper, we study predicting users'' preferences on social groups by considering content information in EBSN, i.e., geographic-social event-based recommendation. Specifically, we consider two types of content information, i.e., the tags and geographical event locations about users/events. We propose the SEGELER (pair-wiSE Geo-social Event-based LatEnt factoR) to model the users behavior considering the information. Experiments on a real-world EBSN social network validate the effectiveness of our proposed approach for both normal users and cold start users.