The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users' ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem. Different from classic recommendation problems (e.g. movies), event recommendation generally faces three complex problems:Heterogeneous social relationships (online and offline) among users, the implicit feedback data and the content-context information of users/events. How to effectively fuse this information for event recommendation is a common concern for scholars in this field. This work presents a Bayesian latent factor model that combines users/items content-context information and heterogeneous social information for event recommendation. Experimental results on several real-world datasets demonstrate the proposed method can efficiently tackle with implicit feedback characteristic for event recommendation.