A Clustering Based Social Matrix Factorization Technique for Personalized Recommender Systems
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Recently, a new paradigm of social network based recommendation approach has emerged wherein struc- tural features from social network turned out to be an effective measure to improve the efficacy of the al- gorithms. However, these approaches assume a user is impacted by all his social connections and com- pletely ignore their preferential similarity, which is crucial for personalized recommendations. Herein, we address this pivotal issue and propose a two-stage clustering based matrix-factorization algorithm, “Cluster REfinement on Preference Embedded MF (CREPE MF)” using a subgraph of social network that integrates the preferential similarity score. Clustering has been applied first on the user followed by the item based on ratings. The proposed algorithm has been systematically evaluated with state-of-the-art algorithms in terms of prediction accuracy and runtime complexity using real-world Yelp dataset. Gratifyingly, our ap- proach outperforms the state-of-the-art algorithms with up to 12.97% and 29.60% improvements in RMSE and runtime, respectively.
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