关键词: 协同过滤, 推荐算法, 多分类器, 相似度

Abstract:

In order to solve the problem of easy to fall into the trouble of “single indicator optimal” and low recommendation accuracy for tradition collaborative filtering recommendation algorithm based on single-class classification, hybrid collaborative filtering recommendation algorithm based on KNN-GBDT is proposed. The algorithm used K-nearest neighbor method to filter out multiple candidate nearest neighbors and comprehend advantages of ensemble learning. It uses multiple classifier to ensemble multiple recommendation results. In order to get more potential information about the target user, consider items weight with only single user rating into the similarity calculation formula. Experimental results show that the algorithm can reduce the sparse of data set which is formed by the target user and the candidate nearest, and improves the recommendation accuracy.

Key words: collaborative filtering, recommendation algorithm, multiple classifier, similarity