Research on Entropy-based Collaborative Filtering Algorithm

Based on the brief introduction to the user-based and item-based collaborative filtering algorithms, the problems related to the two algorithms are analyzed, and a new entropy-based recommendation algorithm is proposed. Aimed at the drawbacks of traditional similarity measurement methods, we put forward an improved similarity measurement method. The entropy-based collaborative filtering algorithm contributes to solving the cold-start problem and discovering users´ hidden interests. Using the practical data obtained from Movielens Website and MAE metrics for accuracy measure, three different collaborative filtering recommendation algorithms are compared through experiments. The results show that the entropy-based algorithm provides better recommendation quality than user-based algorithm and achieves recommendation accuracy comparable to the item-based algorithm. The experimental solution, the advantages of the entropy-based algorithm and future work are discussed in detail.