Abstract: The traditional User-based Collaborative Filtering (UCF) models do not consider the attributes of items fully in the process of measuring the similarity of users. In view of the drawback, this paper proposed two collaborative filtering recommendation models considering item attributes. Firstly, the models optimized the rating-based similarity between users, and then summed the rating numbers of different items by users according to item attributes, in order to obtain the optimized and attribute-based similarity between users. Finally, the models coordinated the two types of similarity measurements by a self-adaptive balance factor, to complete the item prediction and recommendation process. The experimental results demonstrate that the newly proposed models not only have reasonable time costs in different data sets, but also yield excellent improvements in prediction accuracy of ratings, involving an average improvement of 5%, which confirms that the models are efficient in improving the accuracy of user similarity measurements.

Key words: recommender system, collaborative filtering, rating similarity, item attribute, similarity model

摘要： 针对传统的基于用户的协同过滤(UCF)模型在相似性度量过程中没有充分考虑项目属性的问题，提出了两种考虑项目属性的协同过滤推荐模型。模型首先对用户评分相似性进行优化；然后从项目属性的角度统计用户关于不同项目的评价次数，获得优化的基于项目属性的用户相似性；最后通过自适应平衡因子协调处理两方面的相似性结果进行项目预测与推荐。实验结果表明，在不同的数据集中，新提出的模型不仅时间花费较为合理，而且评分预测准确性明显提高，平均提高了5%，从而证明了模型在改进用户相似性度量精度方面的有效性。

关键词: 推荐系统, 协同过滤, 评分相似性, 项目属性, 相似性模型