Abstract: Event based social networks (EBSNs) provides users with online to offline social services. Users can organize events online and participate in them offline. Particularly, social event recommendation is a hot issue in this field. The existing studies have mainly focused on factors-based or graph-based social event recommendation, which can hardly exploit the semantic information in the social networks thoroughly. To address this problem, a meta-path based social event recommendation method is proposed. The meta-path contains rich semantic information, being an important feature of the heterogeneous information networks. Firstly, a heterogeneous network is constructed, and then various candidate meta-paths are utilized to calculate the correlation matrix between the users and events. On this basis, the weight of each meta-path is solved by optimization method. Accordingly, the social event recommendation is realized in terms of the linear combination of correlation matrix and meta-path weights. The algorithm is validated on the real dataset of Meetup, the most popular event-based social network platform. Meanwhile, various metrics are explored to compare the recommendation methods. The experimental results show that the proposed algorithm outperforms the comparative ones.