传统协同过滤推荐算法中经常因用户评分矩阵极端稀疏而导致相似性度量方法不准,推荐质量不高,针对这一问题,提出一种基于项目属性和云填充的协同过滤推荐算法.利用云模型对用户评分矩阵进行填充,在填充矩阵基础上,利用传统的相似性计算方法得到项目之间的评分相似性,同时结合项目属性,计算项目的属性相似性,通过加权因子得到项目的最终相似性,从而形成一种新的相似性度量方法.实验结果表明,提出的算法可有效解决传统方法中由于数据稀疏所导致的相似性度量不准确的问题,并显著地提高了算法的推荐精度.%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.
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