Matrix Factorization Enriched with Item Features

被引：0

作者：

Zhang, Haiyang ^{[1
]}

Ganchev, Ivan ^{[2
,3
,4
]}

Nikolov, Nikola S. ^{[4
]}

机构：

[1] Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England

[2] Univ Plovdiv Paisii Hilendarski, Plovdiv, Bulgaria

[3] Bulgarian Acad Sci, Inst Math & Informat, Sofia, Bulgaria

[4] Univ Limerick, CSIS Dept, Limerick, Ireland

来源：

2ND INTERNATIONAL CONFERENCE ON MATHEMATICS AND COMPUTERS IN SCIENCE AND ENGINEERING (MACISE 2020) | 2020年

关键词：

recommendation model; collaborative filtering (CF); matrix factorization (MF); data sparsity; cold start; RECOMMENDER SYSTEMS;

D O I：

10.1109/MACISE49704.2020.00020

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

This paper(1) presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the coldstart item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better prediction accuracy than some of the popular state-of-theart MF-based recommendation models.

引用

页码：77 / 80

页数：4

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