Recommendation system for grocery store considering data sparsity

被引:6
作者
Sano, Natsuki [1 ]
Machino, Natsumi [2 ]
Yada, Katsutoshi [3 ]
Suzuki, Tomomichi [1 ]
机构
[1] Tokyo Univ Sci, Noda, Chiba 2788510, Japan
[2] Recruit Commun Co Ltd, Chuo Ward 1040054, Japan
[3] Kansai Univ, Suita, Osaka 5648680, Japan
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015 | 2015年 / 60卷
关键词
Recommendation; grocery store; data sparsity; singlur value decomposition; nonlinear principal component analysis;
D O I
10.1016/j.procs.2015.08.216
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In grocery stores, large-scale transaction data with identification, such as point of sales (POS) data, is being accumulated as a result of the introduction of frequent shopper programs. We propose two recommendation systems based on transaction data of a grocery store. In recommending product items in grocery stores, data sparsity is a problem. This is because individual customers only purchase very few of the total number of product items a store sells. We evaluate various recommendation methods including SVD-type recommendation based on real POS data and summarize methods suitable for the proposed recommendation systems. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:1406 / 1413
页数:8
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