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Information Journal Paper

Title

Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks

Pages

  605-618

Abstract

 Recommender systems has shown as effective tools that are proposed for helping users to select their interested items. collaborative filtering is a well-known and frequently used recommender system applied successfully in many e-commerce websites. However, these systems have poor performance while facing cold-start users (items). To address such issues, in this paper, a social regularization method is proposed which combines the social network information of users in a nonnegative matrix factorization framework. The proposed method integrates multiple information sources such as user-item ratings and trust statements to reduce the cold-start and data sparsity issues. Moreover, the alternating direction method is used to improve the convergence speed and reduce the computational cost. We use two well-known datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation methods for recommendation in social networks.

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    APA: Copy

    PARVIN, H., MORADI, P., & ESMAEILI, SH.. (2020). Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 50(2 (92) ), 605-618. SID. https://sid.ir/paper/386042/en

    Vancouver: Copy

    PARVIN H., MORADI P., ESMAEILI SH.. Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING[Internet]. 2020;50(2 (92) ):605-618. Available from: https://sid.ir/paper/386042/en

    IEEE: Copy

    H. PARVIN, P. MORADI, and SH. ESMAEILI, “Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks,” TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 50, no. 2 (92) , pp. 605–618, 2020, [Online]. Available: https://sid.ir/paper/386042/en

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