Recommender Systems Based on User Latent Trust and Trusted Relationship
YU Jiaxuan
LI Ming
DING Derui
Abstract：The existed recommender system models are relatively conservative due to the dramatic increase in the number of users and entries,and the potential correlation information between users without full consideration.As such,this paper proposes a novel model of recommender systems based on that utilizes the latent trust and trusted relationship between users.Specifically,the model has the following characteristics.The matrix of potential trust and trusted relationship between users can be obtained by intro-ducing the graph Laplacian regularization to ensure the user structure information in the latent trust space.The consistency of similar users'preferences for scoring items is ensured via knowledge transfer of the obtained relationship matrix to the decomposition pro-cess of the scoring matrix as well as the utilization of the locally learned social regulation of similar users,and the computational complexity caused by knowledge transfer is effectively reduced by resorting to a new single-factor-based variable update principle.Finally,the experimental results on six real datasets show that the model proposed in this paper can achieve high prediction accura-cy while ensuring computational efficiency.
Keywords：recommender systemlatent trust relationshiplatent trusted relationshipgraph Laplacian regularization
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 652-659 )