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Shahrokh Esmaeili

Shahrokh Esmaeili

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 15834719000
Faculty: Faculty of Science
Address: Department of Mathematics University of Kurdistan
Phone: 08733624133

Research

Title
AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS
Type
Presentation
Keywords
Recommender systems, social Trust, matrix Factorization, distrust Relationships, collaborative filtering.
Year
2018
Researchers Hashem Parvin ، Parham Moradi ، Shahrokh Esmaeili ، Mahdi Jalili

Abstract

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.