The newly emerging event-based social network (EBSN) based on the event as the core combines the online relationship with offline activities to promote the formation of real and effective social relationship among users. However, excessive activity information would make users difficult to distinguish and choose. The context-aware local event recommendation is an effective solution for the information overload problem, but most of existing local event recommendation algorithms only learns users' preference for contextual information indirectly from statistics of historical event participation and ignores latent correlations among them, which impacts on recommendation effectiveness. To take full advantage of latent correlations between users' event preference and contextual information, the proposed collective contextual relation learning (CCRL) algorithm models relations among users' participation records and related contextual information such as event organizer, description text, venue, and starting time. Then multi-relational Bayesian personalized ranking (MRBPR) algorithm is adapted for collective contextual relation learning and local event recommendation. Experiment results on Meetup dataset demonstrate that proposed algorithm outperforms state-of-the-art local event recommendation algorithms in terms of many metrics.