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Although recommendations have been proposed to help users find communities of interest, these methods ignore or exclude heterogeneous interactions between users and communities. In addition, widely used meta-paths help capture the complex semantic relation among entities but heavily rely on domain knowledge. In this study, we propose a novel recommendation model based on informative meta-path discovery in heterogeneous information networks and deep learning. Users, communities, relevant items and their relations are considered as entities in a heterogeneous information network, from where informative meta-paths are extracted on the basis of information theory to measure user-community similarities. Finally, similarities are incorporated in a deep learning model to predict whether target users join candidate communities. The proposed recommendation model is evaluated and compared against baseline methods using two data sets. 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