关键词: 协同过滤, 稀疏数据, 云填充, 评分相似性, 属性相似性, 相似性度量

Abstract: The user rating data in traditional collaborative filtering recommendation algorithm are extremely sparse, which results in bad similarity measurement and poor recommendation quality. In view of this problem, this paper presented an improved collaborative filtering algorithm, which was based on item attribute and cloud model filling. The algorithm proposed a new similarity measurement method, using the data filling based on cloud model and the similarity of the item's attributes. The new method computed the rating similarity by using the traditional similarity measurement on the basis of the filling matrix and computed the attributing similarity by using item's attributes, then got the last similarity by using weighting factor. The experimental results show that this method can efficiently solve the problem of similarity measurement inaccuracy caused by the extreme sparsity of user rating data, and provide better recommendation results than traditional collaborative filtering algorithms.

Key words: collaborative filtering, sparse data, cloud model filling, rating similarity, attributing similarity, similarity measure