This paper presents a multi-label classification based CF framework, MLCF, which improves the quality of recommendation in the presence of data sparsity by learning over a heterogeneous information network consisting of a rating bipartite graph, a user graph and an item graph. MLCF is novel by three unique features. First, we explore the latent correlations among users and items w.r.t. a given set of K semantic categories beyond user-item ratings by employing multi-label clustering of items, and multi-label classification of users and rating-based similarities on the heterogeneous network. Second, based on the user/item/similarity multi-label clustering/classification, we propose a fine-grained multi-label classification based rating similarity measure to capture the class-specific relationships between users by introducing a novel concept of vertex-edge homophily. Third but not the least, we propose to integrate two kinds of multi-label classification based CF models focusing on rating and social information into a unified prediction model.
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