计算机应用研究2016，Vol.33Issue(3)：669-672,4.DOI:10.3969/j.issn.1001-3695.2016.03.007
结合类别偏好信息的item-based 协同过滤算法
Improved item-based collaborative filtering algorithm combined with class preference information
摘要
Abstract
The traditional item-based collaborative filtering(CF)algorithm computes item-item similarity offline,so it en-hances the real-time performance of recommender system.However,item-based CF algorithm still suffers from the data sparsity problem,as a result that the recommendation quality is poor.To address this issue,this paper proposed a novel CF algorithm combined with class preference information.The proposed algorithm first found out candidate neighbors who were similar to the target item in class preference.Then it searched for nearest neighbors in the candidate neighbor set,which eliminated the in-terference of the items those had few co-ratings with the target item.Experimental results based on MovieLens dataset show that the recommendation quality of the new algorithm is significantly improved compared with traditional item-based CF algorithm.关键词
推荐系统/协同过滤/类别偏好/相似性Key words
recommender system/collaborative filtering/class preference/similarity分类
信息技术与安全科学引用本文复制引用
冷亚军,陆青,张俊岭..结合类别偏好信息的item-based 协同过滤算法[J].计算机应用研究,2016,33(3):669-672,4.基金项目
国家自然科学基金资助项目（71201145）；上海市教育委员会科研创新资助项目（15ZS064）；上海电力学院科研基金资助项目（K2014-037）；上海高校青年教师培养资助计划 （）
