Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
Source abstract
Abstract
Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: One involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Privacy-Preserving Technologies in Data · Caching and Content Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, School of Software, Beijing University of Posts and Telecommunications, Beijing 100876, China
References (28)
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