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.