摘要:基于事件社会网络（event-based social network，简称EBSN）是一种结合了线上网络和线下网络的新型社会网络，近年来得到了越来越多的关注，已有许多国内外重要研究机构的研究者对其进行研究并取得了许多研究成果.在EBSN推荐系统中，一个重要的任务就是设计出更好、更合理的推荐算法以提高推荐精确度和用户满意度，其关键在于充分结合EBSN中的各种上下文信息去挖掘用户、事件和群组的隐藏特征.主要对EBSN推荐系统的最新研究进展进行综述.首先，概述EBSN的定义、结构、属性和特征，介绍EBSN推荐系统的基本框架，并分析EBSN推荐系统与其他推荐系统的区别；其次，对EBSN推荐系统的主要推荐方法和推荐内容进行归纳、总结和对比分析；最后，分析EBSN推荐系统的研究难点及其发展趋势，并给出总结.



Abstract:Event-based social network (EBSN) is a new type of social network combining online network and offline network, which has received more and more attentions in recent years. There have been many researchers in important research institutions domestic and abroad to study it and they have achieved a lot of research results. In an EBSN recommendation system, one important task is to design better and more reasonable recommendation algorithms to improve recommendation accuracy and user satisfaction. The key is to fully combine various contextual information in EBSN to mine the hidden features of users, events, and groups. This study mainly reviews the latest research progress of the EBSN recommendation system. First, the definition, structure, attributes, and characteristics of EBSN are outlined, the basic framework of EBSN recommendation systems is introduced, and the differences between EBSN recommendation system and other recommendation systems are analyzed. Secondly, the main recommendation methods and recommended contents of the EBSN recommendation system are generalized, summarized, compared, and analyzed. Finally, the research difficulties and development future trends of the EBSN recommendation system are analyzed, and conclusions of the study are drawn.



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