Abstract: Aiming at the noise data problem in collaborative filtering recommendation, a user entropy model was put forward. The user entropy model combined the concept of entropy in the information theory and used the information entropy to measure the content of user information, which filtered the noise data by calculating the entropy of users and getting rid of the users with low entropy. Meanwhile, combining the user entropy model with the item timeliness model, the item timeliness model got the timeliness of item by using the contextual information of the rating data, which alleviated the data sparsity problem in collaborative filtering algorithm. The experimental results show that the proposed algorithm can effectively filter out noise data and improve the recommendation accuracy, its recommendation precision is increased by about 1.1% compared with the basic algorithm.

Key words: recommender system, collaborative filtering, noise data, data sparsity, information entropy, timeliness

摘要： 针对协同过滤推荐算法存在的噪声数据问题，提出了用户信息熵模型。用户信息熵模型结合信息论中信息熵的概念，采用信息熵的大小衡量用户信息的含量，利用用户评分数据得到用户的信息熵，过滤信息熵低的用户，从而达到过滤噪声数据的目的。同时，将用户信息熵模型和项目时效性模型相结合，项目时效性模型利用评分数据上下文信息获得项目的时效性，能有效缓解协同过滤的数据稀疏性问题。实验结果表明提出的算法能有效过滤噪声数据，提高推荐精度，与基础算法相比，推荐精度提高了1.1%左右。

关键词: 推荐系统, 协同过滤, 噪声数据, 数据稀疏性, 信息熵, 时效性