Recommendation Model Based on Probabilistic Matrix Factorization and Rated Item Relevance

Personalized recommendation has become indispensable in today's information society. Personalized recommendations play a significant role for both information producers and consumers. Studies have shown that probability matrix factorization can improve personalized recommendation performance. However, most probability matrix factorization models ignore the effect of item-implicit association and user-implicit similarity on recommendation performance. To overcome this lack, we propose a recommendation model based on probability matrix factorization that considers the correlation of user rating items. Our model uses the resource allocation of the bipartite graphs and the random walk of meta-paths in heterogeneous networks to determine the implicit association of items and the implicit similarity of users, respectively. Thus, the final item association and user similarity are obtained. The final item and user similarity relationships are integrated into the probability matrix factorization model to obtain the user's prediction score for a specific project. Finally, we validated the model on the Delicious-2k, Movielens-2k and last.fm-2k datasets. The results show that our proposed algorithm model has higher recommendation accuracy than other recommendation algorithms.