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In this paper, we propose a hybrid approach for music recommendation. Firstly, we describe an approach for creating music recommendations based on user-supplied tags that are augmented with a hierarchical structure extracted for top level genres from Dbpedia. In this structure, each genre is represented by its stylistic origins, typical instruments, derivative forms, sub genres and fusion genres. We use this well-organized structure in dimensionality reduction in user and item profiling. We compare two recommenders, one using our method and the other using Latent Semantic Analysis (LSA) in dimensionality reduction. The recommender using our approach outperforms the other. In addition to different dimensionality reduction methods, we evaluate the recommenders with different user profiling methods. Moreover, our approach collects personal interests (favorite movies and television series) from the Face book profiles. These user profiles are then used to find the similarity between users. At the end, items belonging to the most similar users' profiles and having a high score against users' profiles are recommended. Thus, we have focused on a hybrid system using tag-based contextual information of music tracks and user interests acquired from Face book profiles. Initial results are promising such that using similarities of users affects the recommendation positively.