《计算机应用研究》|Application Research of Computers

自适应用户的Item-based协同过滤推荐算法

User-adaptive Item-based collaborative filtering recommendation algorithm

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作者 汪从梅,王成良,徐玲
机构 重庆大学a.计算机学院;b.软件学院,重庆400044
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文章编号 1001-3695(2013)12-3606-04
DOI 10.3969/j.issn.1001-3695.2013.12.023
摘要 传统Item-based协同过滤算法计算两个条目间相似性时, 将每个评分视为同等重要, 忽略了共评用户(对两个条目共同评分的用户)与目标用户间的相似性对条目间相似性的影响。针对此问题, 提出了一种自适应用户的Item-based协同过滤算法。该算法将共评用户与目标用户的相似性作为共评用户评分重要性的权重, 以实现针对不同的目标用户, 为目标条目选择不同的、适合目标用户的最近邻居集, 从而提高推荐准确性。实验结果表明, 提出的算法可以显著提高推荐系统的推荐质量。
关键词 推荐系统;协同过滤;Item-based;自适应用户;条目相似性;信息过载
基金项目 中央高校基本科研业务费科研专项基金资助项目(CDJZR11090001)
本文URL http://www.arocmag.com/article/01-2013-12-023.html
英文标题 User-adaptive Item-based collaborative filtering recommendation algorithm
作者英文名 WANG Cong-mei, WANG Cheng-liang, XU Ling
机构英文名 a. College of Computer Science, b. School of Software Engineering, Chongqing University, Chongqing 400044, China
英文摘要 The traditional Item-based collaborative filtering algorithm regards every rating as equal importance when calculating the similarity between items, and ignores the impact of the similarity between co-rated users (users co-rate both two items) and target user on the similarity between items. This paper proposed a user-adaptive Item-based collaborative filtering recommendation algorithm, in which the rating of a co-rated user on an item was weighted by the user similarity between the co-rated user and target user, in order to select different neighbors of a certain target item for different target users, and so as to improve the recommendation accuracy. The experiment results suggest that the proposed algorithm can efficiently improve the recommendation quality.
英文关键词 recommender system; collaborative filtering; Item-based; user-adaptive; item similarity; information overloading
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收稿日期
修回日期
页码 3606-3609
中图分类号 TP312;TP301.6
文献标志码 A