مشخصات پژوهش

عنوان	AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Recommender systems, social Trust, matrix Factorization, distrust Relationships, collaborative filtering.
چکیده	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.
پژوهشگران	مهدی جلیلی (نفر چهارم)، شاهرخ اسمعیلی (نفر سوم)، پرهام مرادی دولت آبادی (نفر دوم)، هاشم پروین (نفر اول)