计算机应用研究2013，Vol.30Issue(9)：2602-2605,4.DOI:10.3969/j.issn.1001-3695.2013.09.010
分步填充缓解数据稀疏性的协同过滤算法
Collaborative filtering algorithm based on two-step filling for alleviating data sparsity
摘要
Abstract
To overcome the data sparsity of traditional collaborative filtering algorithm which can cause inaccuracy during finding the nearest-neighbors,the paper came up with a new method combining conditional probability with traditional collaborative algorithm whose neighbors was not always k.The algorithm' core was that the last data matrix was calculated by two-step filling.The first step,it accepted the users whose similarity and the number of both-rated items met the standard as the target user's neighbors and then calculated the value and filled the unrated-items.The second step would fill the left unrated-items relying on the data matrix filled by the first step.Experimental results show that this algorithm can find reliable neighbors,alleviate the data sparsity and achieve better prediction accuracy obviously.关键词
协同过滤/条件概率/推荐系统/数据稀疏/分步填充Key words
collaborative filtering/ conditional probability/ recommendation system/ data sparsity/ two-step filling分类
信息技术与安全科学引用本文复制引用
张玉芳,代金龙,熊忠阳..分步填充缓解数据稀疏性的协同过滤算法[J].计算机应用研究,2013,30(9):2602-2605,4.基金项目
国家自然科学基金资助项目(71102065) （71102065）
