A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. Among them, matrix decomposition method mainly uses the interactions records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a UISVD++ model that fuses the type attributes of movies and the age attributes of users into MF framework. Project and user representations in MF are enriched by projecting each user's age attribute and each movie's type attribute into the same potential factor space as users and items. Finally, the MovieLens-100K and MovieLens-1M datasets were used to compare with the traditional SVD++ and other models. The results show that the proposed model can achieve the best recommendation performance and better predict user ratings under all backgrounds.