计算机应用研究2018，Vol.35Issue(3)：711-716,6.DOI:10.3969/j.issn.1001-3695.2018.03.015
电子商务中隐空间多源迁移协同过滤
Latent multi-source transfer collaborative filtering in electronic commerce
摘要
Abstract
While collaborative filtering(CF) algorithm shave been widely applied in recommender systems,the sparsity of the target rating data issue was still a crucial bottleneck for most existing CF methods.To this end,this paper proposed a novel CF algorithm——LMTCF,to address the sparse collaborative filtering problem.In certain optimal latent subspace,LMTCF aimed to reduce the sparsity in target data by transferring knowledge (also called as the latent factors of users and items) from multiple dense auxiliary data sources as well as preserving the local geometrical structure of the target data.Besides,LMTCF could additionally address two key issues effectively,i.e.,negative transfer and inadequate transfer learning,thus allowing more positive knowledge transferred across domains to reduce the sparsity of target data.Experiments on two benchmark datasets demonstrate that this method significantly outperforms state-of-the-art CF methods.关键词
协同过滤推荐/稀疏性/多源迁移学习/隐空间Key words
collaborative filtering recommendation/sparsity/multi-source transfer learning/latent subspace分类
信息技术与安全科学引用本文复制引用
龚松杰,丁佩芬,文世挺..电子商务中隐空间多源迁移协同过滤[J].计算机应用研究,2018,35(3):711-716,6.基金项目
浙江省社科规划课题成果项目(14NDJC157YB) （14NDJC157YB）
宁波市软科学项目(2015A10025) （2015A10025）
浙江省教育科学规划重点资助项目(2015SB103) （2015SB103）
国家教育部人文社会科学研究一般项目—青年基金资助项目(16YJCZH112) （16YJCZH112）
