关键词: 协同过滤, 推荐系统, 朴素贝叶斯, 互信息, 平均绝对误差

Abstract: Collaborative filtering is used extensively in personalized recommendation systems. With the development of E-commence, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparseness of user rating data. To address the problem a collaborative filtering recommendation algorithm based on naive Bayesian method was proposed. The algorithm used improved weighted Bayesian method to predict the rating of unrated items. Through predicting unrated data, the sparseness of rating data problem had been alleviated and the accurate degree of searching nearest neighbor items had been improved simultaneously. The experiment shows that the measure provides better recommendation results for the system.

Key words: Collaborative filtering, Recommendation system, Naive Bayesian, Mutual information, Mean absolute error