Event-Based social networks (EBSNs) have experienced rapid growth in people's daily life. Hence, event recommendation plays an important role in helping people discover interesting online events and attend offline activities face to face in the real world. However, event recommendation is quite different from traditional recommender systems, and there are several challenges:(1) One user can only attend a scarce number of events, leading to a very sparse user-event matrix; (2) The response data of users is implicit feedback; (3) Events have their life cycles, so outdated events should not be recommended to users; (4) A large number of new events which are created every day need to be recommended to users in time. To cope with these challenges, this article proposes to jointly model heterogeneous social and content information for event recommendation. This approach explores both the online and offline social interactions and fuses the content of events to model their joint effect on users' decision-making for events. Extensive experiments are conducted to evaluate the performance of the proposed model on Meetup dataset. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods.