Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

Personalized recommendation has gained widespread attention in the academic and industrial fields to minimize information overload and has produced good benefits. Current research shows that social recommendations that effectively utilize user trust relationships can solve data sparsity and cold start problems common in traditional collaborative filtering algorithms. However, existing social recommendation models have focused only on direct trust relationships between users and have ignored indirect trust relationships and item correlations. To address these problems, we propose a probabilistic matrix factorization-based recommendation model based on trust relationships, interest mining, and item correlation. The proposed recommendation model considers the direct and indirect trust relationships between users, the similarities in users' preferences for item attributes, and the correlations between items. Finally, the rating of the item is predicted by the target user and provides the target user with personalized item recommendations. We evaluate the recommendation performances of the proposed recommendation model on the FilmTrust and the CiaoDVD datasets and find that it alleviates the user's cold start problem and provides higher recommendation accuracy and diversity than popular algorithms.

Automated summary

Personalized recommendation has gained attention in academia and industry for minimizing information overload and producing good results. Social recommendation models that utilize user trust relationships effectively have been found to solve common problems in traditional collaborative filtering algorithms, such as data sparsity and cold start. However, existing models have overlooked indirect trust relationships and item correlations. To address these issues, the proposed probabilistic matrix factorization-based recommendation model considers direct and indirect trust relationships, user preference similarities, and item correlations. Evaluation on FilmTrust and CiaoDVD datasets demonstrates that the model alleviates the user's cold start problem and provides higher accuracy and diversity in recommendations compared to popular algorithms.