计算机应用研究2013，Vol.30Issue(6)：1688-1691,4.DOI:10.3969/j.issn.1001-3695.2013.06.022
一种基于Sigmoid函数的改进协同过滤推荐算法
Improved collaborative filtering recommender algorithm based on Sigmoid function
摘要
Abstract
As the rapid development of the electronic commerce and social network,recommender systems have become one of the most important research areas in data mining field.Recommender systems can identify users' interest out of humorous information in order to provide personalized service.Collaborative filtering (CF) is efficient in extracting users' preferences and making proper recommendations.To address the data sparsity problem of classic CF algorithms and improve the performance,this paper introduced an improved algorithm based on Sigmoid function.Different items were modeled with Sigmoid function in order to capture their popularity,while different users were modeled to map ratings into preferences.Predictions were made according to that preferences should keep consistent with popularities.Experimental results on two real world datasets show the proposed method can alleviate the sparsity problem and are effective to improve the performance of classic CF algorithms.关键词
推荐系统/协同过滤/稀疏性问题/Sigmoid函数Key words
recommender systems/ collaborative filtering(CF) / sparsity problem/ Sigmoid function分类
信息技术与安全科学引用本文复制引用
方耀宁,郭云飞,扈红超,兰巨龙..一种基于Sigmoid函数的改进协同过滤推荐算法[J].计算机应用研究,2013,30(6):1688-1691,4.基金项目
国家"973"计划资助项目(2012CB315901) （2012CB315901）
国家"863"计划资助项目(2011AA01A103) （2011AA01A103）
