Weighted Matrix Factorization Recommendation Model Incorporating Social Trust

Abstract

Utilizing user social networks can unearth more effective information to improve the performance of traditional recommendation models. However, existing models often solely utilize trust relationships and information, lacking efficient models that integrate with user historical ratings, as well as methods for accurately adjusting weights and filtering interfering data. This leads to the models’ inability to efficiently use social networks to enhance recommendation accuracy. Therefore, this paper proposes a novel trust-based weighted matrix factorization recommendation model, Trust-WMF. Initially, the model preliminarily calculates users’ predicted ratings for items using trust relationships in the social network and user similarity relations in user historical ratings, simultaneously dynamically integrating these two parts of predicted ratings using adaptive weights. Subsequently, the ratings are incorporated into an improved weighted matrix factorization model, allowing them to have different weights in training compared to user historical ratings. This enriches matrix information and reduces the impact of noise data, thus forming an efficient, unified, and trustworthy recommendation model. Finally, the model was compared and validated on the Epinions and Ciao datasets, with results confirming its efficiency.