A Rating Bias Formulation based on Fuzzy Set for Recommendation

In recommender systems, the user uncertain preference results in unexpected ratings. Previous approaches (e.g., BiasMF) only adjust the rating value based on the bias vector, ignoring the uncertainty of rating. This paper makes an initial attempt in integrating the influence of user uncertain degree and user rating bias into the matrix factorization framework, simultaneously. An approach based on fuzzy set, called fuZzy Matrix Factorization (ZMF), is proposed. Specifically, a fuzzy set of like is defined for each user, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Then, the user uncertain preference matrix is obtained, which could explain and represent the user bias and uncertainty effectively. Furthermore, to enhance the computational impact on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Besides, the proposed approach could be extended to others due to independency on additional data sources. Experimental results on three datasets show that ZMF produces an effective improvement.