Collaborative filtering recommendation algorithm based on interval-valued fuzzy numbers

Most collaborative filtering recommendation algorithms use crisp ratings to represent the users’ preferences. However, users’ preferences are subjective and changeable, crisp ratings can’t measure the uncertainty of users’ preferences effectively. In order to solve this problem, this paper proposes the interval-valued triangular fuzzy rating model. This model replaces crisp ratings with interval-valued triangular fuzzy numbers on the basis of users’ rating statistics information, which can measure the users’ preferences in a more reasonable way. Based on this model, the collaborative filtering recommendation algorithm based on interval-valued fuzzy numbers is designed. The algorithm calculates the users’ similarity by the interval-valued triangular fuzzy numbers, and takes the ambiguity of ratings into consideration in the prediction stage. Our experiments prove that, compared with other fuzzy and traditional algorithms, our algorithm can increase the prediction precision and rank accuracy effectively with a little time cost, and has an obvious advantage when implemented in a sparse dataset which has more users than items. Thus our method has strong effectiveness and practicability.