Multimedia Tools and Applications https://doi.org/10.1007/s11042-020-08884-9 An effective content-based event recommendation model Thanh Trinh 1 · Dingming Wu 1 · Ruili Wang 2 · Joshua Zhexue Huang 1 Received: 3 July 2019 / Revised: 8 January 2020 / Accepted: 27 March 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Event-based social networks (EBSNs) facilitate people to interact with each other by sharing similar interests in online groups or taking part in offline events together. Event recom- mendation in EBSNs has been studied by many researchers. However, the problem of recommending the event to the top N active-friends of the key user has rarely been studied in EBSNs. In this paper, we propose a new method to solve this problem. In this method, we first construct an association matrix from the content of events and user features. Then, we define a new content-based event recommendation model, which combines the matrix, spatio-temporal relations and user interests to recommend an event to the active-friends of a key user. A series of experiments were conducted on real datasets collected from Meetup, and the comparison results have demonstrated the effectiveness of the new model. Keywords EBSNs · Social networks · Topic model · Recommendation 1 Introduction Event-based social networks (EBSNs) have become popular in the last couple of years, e.g., Meetup 1 and Douban 2 , which provide platforms for those who interact with each other in 1 www.meetup.com 2 www.douban.com  Dingming Wu dingming@szu.edu.cn Thanh Trinh tthanh@szu.edu.cn Ruili Wang Ruili.wang@massey.ac.nz Joshua Zhexue Huang zx.huang@szu.edu.cn 1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China 2 School of Natural and Computational Sciences, Massey University, Auckland, New Zealand