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Start Date

16-8-2018 12:00 AM

Description

Recently, a new paradigm of social network based recommendation approach has emerged wherein structural features from social network turned out to be an effective measure to improve the efficacy of the algorithms. However, these approaches assume a user is impacted by all his social connections and completely 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 approach 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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Aug 16th, 12:00 AM

A Clustering Based Social Matrix Factorization Technique for Personalized Recommender Systems

Recently, a new paradigm of social network based recommendation approach has emerged wherein structural features from social network turned out to be an effective measure to improve the efficacy of the algorithms. However, these approaches assume a user is impacted by all his social connections and completely 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 approach outperforms the state-of-the-art algorithms with up to 12.97% and 29.60% improvements in RMSE and runtime, respectively.