摘要： 针对用户评分数据的极端稀疏性和传统计算项目相似性方法存在的弊端，提出一种基于云模型的推荐算法，利用云模型计算项目间的相似度来预测用户对未评分项目的评分，再通过云模型计算用户间的相似度，得到目标用户的最近邻居。实验结果表明，该算法不仅能有效解决用户评分数据的稀疏性问题，还能提高推荐系统的推荐质量。

关键词: 云模型, 协同过滤, 项目相似性

Abstract: Aiming at the sparsity of user rating data and the drawbacks of traditional similarity measure methods, a novel recommendation algorithm based on cloud model is proposed. It predicts the item ratings that users have not rated by calculating the item similarity based on the cloud model. It calculates the similarity of users by using the cloud model to find the target users’ neighbors. Experimental results show that the algorithm can not only efficiently lower sparsity of rating data but also improve the recommend quality of the recommender system.

Key words: cloud model, collaborative filtering, item similarity

中图分类号: 

• TP311