关键词: 协同过滤, 推荐算法, 聚类, 随机森林

Abstract: To handle the inefficiency problem of online recommendation of neighborhood-based collaborative filtering algorithms, this paper proposes a method to train a rating prediction model offline. The method firstly reduces the dimensions of the user vectors and the item vectors in the user-item rating matrix, and transforms this matrix so as to use supervised learning models. A random forest model is then trained by using the transformed data, and the online rating prediction is made by the previous trained model without the search of the nearest neighborhoods. The experiment results show that the method performs much better than neighborhood-based collaborative filtering algorithms in term of online recommendation efficiency without decreasing the precision of rating prediction.

Key words: collaborative filtering, recommendation algorithm, clustering, random forests