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

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

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作者 龚松杰,丁佩芬,文世挺
机构 1.浙江工商职业技术学院,浙江 宁波 315012;2.浙江大学宁波理工学院 信息科学与工程学院,浙江 宁波 315100
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文章编号 1001-3695(2018)03-0711-06
DOI 10.3969/j.issn.1001-3695.2018.03.015
摘要 评分数据的极端稀疏性是制约协同过滤(CF)算法在电子商务推荐中有效应用的关键瓶颈。为此,提出一种新颖的隐空间多源迁移协同过滤(latent multi-source transfer collaborative filtering,LMTCF)方法,在某个优化的隐子空间内,LMTCF桥接多个用户/项目源领域隐因子,并保留目标数据的局部几何结构,从而更好地解决协同过滤中存在的数据稀疏性问题,且还能有效克服现有方法存在的负迁移和迁移不充分的问题。在实际基准数据集上的实验结果显示了所提方法明显优于现有相关方法。
关键词 协同过滤推荐;稀疏性;多源迁移学习;隐空间
基金项目 浙江省社科规划课题成果项目(14NDJC157YB)
宁波市软科学项目(2015A10025)
浙江省教育科学规划重点资助项目(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)
本文URL http://www.arocmag.com/article/01-2018-03-015.html
英文标题 Latent multi-source transfer collaborative filtering in electronic commerce
作者英文名 Gong Songjie, Ding Peifen, Wen Shiting
机构英文名 1.ZhejiangBusinessTechnologyInstitute,NingboZhejiang315012,China;2.SchoolofInformationScience&Engineering,NingboInstituteofTechnology,ZhejiangUniversity,NingboZhejiang315100,China
英文摘要 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.
英文关键词 collaborative filtering recommendation; sparsity; multi-source transfer learning; latent subspace
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收稿日期 2016/11/9
修回日期 2016/12/30
页码 711-716
中图分类号 TP301.6
文献标志码 A