关键词: 协同过滤, 兴趣偏好, 相似度计算, 分布式计算

Abstract: Collaborative filtering recommendation algorithm recommends interesting content for users from a massive data resource, by studying the user’s preferences. Measuring similarity of user （resource） is the core of collaborative filtering algorithms. In the large volume of data systems, the accuracy and computational complexity are faced in similarity measuring, which thus affect the recommendation results. This paper proposes an improved collaborative filtering algorithm by extracting multi-valued information of user interest preferences, uses improved Minhash algorithm to measure user similarity, and combines with Mapreduce distributed computing, to generate neighbor rationally and effectively, and finishes user ratings recommendations. Experimental results show that the improved algorithm can improve the recommendation accuracy and efficiency, reduce the recommended time-consuming for large data sets.

Key words: collaborative filtering, interest preferences, similarity calculation, distributed computing