关键词: 项目相似度, 相似度加权, 协同过滤算法, 推荐系统

Abstract: In traditional user similarity function, the weight for each item is the same. However, analyzing traditional collaborative filtering algorithms and practical case, the weight of user’s jointly high scoring item should be higher than the weight of user’s jointly low scoring item. And traditional user similarity function does not take taxa relationship between the items. To address the problem, it proposes a method to weight project and finally obtains a user similarity function which considers the similarity weight of items. The experimental results conducted on the movieLens data sets show that compared with the collaborative filtering algorithm which is based on the traditional user similarity function, the collaborative filtering algorithm which considers the similarity weight of items can significantly improve the ratings prediction accuracy and the quality of the recommendation system.

Key words: item similarity, weighted similarity, collaborative filtering algorithm, recommended system