Intuitionistic fuzzy sets based Bernoulli matrix factorization recommendation algorithm

Existing matrix factorization based collaborative filtering recommendation algorithms mainly utilize users' ratings to evaluate model performance from a quantitative perspective, and never describe users' uncertain preference information from qualitative perspective. Therefore, this paper proposes a Bernoulli matrix factorization recommendation model based on intuitionistic fuzzy sets (IFSs) to make Top-$n$ recommendations for active users from the perspective of fuzzy probability of user preferences. Firstly, the user-item rating matrix is divided into the membership matrix, non-membership matrix and hesitancy matrix according to user preference features and the definition of IFS. Subsequently, the Bernoulli matrix factorization (BeMF) is adopted to fit these matrices in parallel to obtain the optimal latent feature vectors, and their inner products are divided proportionally to get the intuitionistic fuzzy number (IFV) of the active users' preference degree for unrated items. Finally, the recommendation lists are determined according to the ranking rule of the IFV. Experimental results on several benchmark datasets show that the proposed model outperforms other methods in terms of item ranking metrics and effectively improves the recommendation quality. Copyright ©2023 Control and Decision.