基于改进 Canopy 聚类的协同过滤推荐算法.

By establishing the binary relationship between users and information products, the recommender system makes use of the data generated by user behavior to mine the objects that each user is interested in and make recommendations, userbased collaborative filtering has been a mainstream approach in recent years, but it has a limitation : recommendations need to consider all users, and a specific user is often similar to a small number of users. To solve this problem, this paper proposed a collaborative filtering algorithm based on improved Canopy clustering, which combined the user model data density, distance and user activity to calculate the weights of the user, then clustered the user model data, the idea of clustering based on Canopy made one user could belong to different classes, which fit in with situations that users might be interested in multiple areas. Finally, corresponding recommendations were made for each user in Canopy, and it predicted the objects that users might be interested in based on the clustering result and user score. By comparing with other algorithms on two real-world data sets MovieLens and million songs, it verifies that the proposed algorithm can significantly improve the accuracy of the recommender system. [ABSTRACT FROM AUTHOR]