Ratings Distribution Recommendation Model-based Collaborative Filtering Recommendation Algorithm

Abstract

In order to solve the problem of the popular item ratings interfering in similarity calculation, we proposed the ratings distribution recommendation model. Based on this model, we designed a new collaborative filtering algorithm. According to ratings distribution, this algorithm firstly get the amount of information carried (The Shannon Entropy). Then, it calculated the rating weights to filter into traditional similarity calculation. The experimental results show that the algorithm can effectively alleviate the above problem and improve the performance of the algorithm.