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Title
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AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS
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Type
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Presentation
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Keywords
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Recommender systems, social Trust, matrix Factorization, distrust Relationships, collaborative filtering.
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Abstract
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Matrix factorization (MF) has been proved to be an effective approach to build a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of user-item matrix. Moreover, these methods suffer from the scalability issues when applying on large-scale real-world tasks. To tackle these issues, in this paper a social regularization method called TrustRSNMF is proposed that incorporates the social trust information of users in nonnegative matrix factorization framework. The proposed method integrates trust statements along with user-item ratings as an additional information source into the recommendation model to deal with the data sparsity and cold-start issues. In order to evaluate the effectiveness of the proposed method, a number of experiments are performed on two real-world datasets. The obtained results demonstrate significant improvements of the proposed method compared to state-of-the-art recommendation methods.
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Researchers
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Mahdi Jalili (Fourth Researcher), Shahrokh Esmaeili (Third Researcher), Parham Moradi (Second Researcher), Hashem Parvin (First Researcher)
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