2024 : 5 : 18
Shahrokh Esmaeili

Shahrokh Esmaeili

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


Nonnegative matrix factorization regularized with trust relationships for solving cold-start problem in recommender systems
Recommender systems; Matrix factorization, Social trust, Alternating direction method, Collaborative filtering
Researchers Hashem Parvin ، Parham Moradi ، Shahrokh Esmaeili


Matrix factorization has shown as an effective collaborative filtering approach to build a successful recommender system. However, these systems have poor performance while facing cold-start users (items). To tackle this issue, in this paper, a social regularization method called TrustANLF 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. To evaluate the proposed method, several experiments are performed on two real-world datasets. The results report the effectiveness of the proposed method compared to several stateof-the-art methods.