关键词: 协同过滤, 推荐算法, 模糊聚类, 花朵授粉算法

Abstract:

In order to solve the problem of the data sparsity and low recommendation accuracy for traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm for clustering optimization is proposed. According to the difference of the user’s scores, the original rating matrix is preprocessed. And the user category preference matrix, which combined by the user item rating matrix and item type matrix is constructed for reflecting users’ preferences better and alleviating the sparsity of data. Then the fuzzy clustering algorithm optimized by flower pollination is used on the matrix to cluster users and enhancethe clustering effect of users. And the similarity of item preference information and the similarity of item rating matrix are weighted and summed to obtain multiple nearest neighbors. The time factor is integrated to predict the item score of target users and improve the influence of user interest change on the recommendation effect. The experimental results on the MovieLens 100k dataset show that the proposed algorithm mitigates the data sparsity and improves the recommendation accuracy.

Key words: collaborative filtering, recommendation algorithm, fuzzy clustering, flower pollination algorithm