计算机应用研究2012，Vol.29Issue(2)：497-500,4.DOI:10.3969/j.issn.1001-3695.2012.02.026
基于用户兴趣度和特征的优化协同过滤推荐
Optimized collaborative filtering recommendation based on users' interest degree and feature
严冬梅 1鲁城华1
作者信息
- 1. 天津财经大学 理工学院信息科学与技术系,天津300222
- 折叠
摘要
Abstract
Collaborative filtering technology is widely used in personalized recommendation system. In order to make the user' s nearest neighbors set more precise and effective, this paper presented an optimized collaborative filtering recommendation algorithm based on users' interest degree and feature. Firstly, it grouped users through calculating users' interest degree to items. Secondly, it got the value of the users' preferences for items when the users had different characteristics. Finally, it used a new method of calculating the similarity degree to calculate the target users' nearest neighbors set. The result shows that the algorithm enhances the effectiveness and accuracy of the nearest neighbors set, and the recommendation quality has significant improvement than traditional algorithm.关键词
用户兴趣度/用户特征/贝叶斯算法/协同过滤/用户相似度Key words
users ' interest degree/ users ' feature/ Bayesian algorithm/ collaborative filtering ( CF) / similarity between users分类
信息技术与安全科学引用本文复制引用
严冬梅,鲁城华..基于用户兴趣度和特征的优化协同过滤推荐[J].计算机应用研究,2012,29(2):497-500,4.
