关键词: 推荐算法, 协同过滤, [K]-means++, 时间因素, 信任度, 相似度

Abstract:

As an important way of information filtering, collaborative filtering algorithm has attracted more and more attention in the era of big data. However, traditional collaborative filtering algorithm has the problem of low recommendation accuracy due to the serious data sparsity and only considering the scoring similarity between users. This paper proposes an improved collaborative filtering algorithm. Firstly, [K]-means++ algorithm is used to cluster the user attributes, so as to reduce the sparsity of data. Secondly, considering that the user interest will change dynamically with time, this paper introduces the time factor into the traditional scoring similarity. Then, the trust error is introduced into the trust relationship between users, so as to improve the user trust. Finally, the scoring similarity based on the time factor and improved user trust are integrated to improve the calculation accuracy of user similarity. The simulation results on the MovieLens dataset show that the proposed algorithm can effectively improve the prediction accuracy.

Key words: recommendation algorithm, collaborative filtering, [K]-means++, time factor, trust, similarity