買い物経路のベクトル化に基づく顧客判別アプローチおよび主成分回帰を用いた適用例

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2012/12/01

タイトル(英語): An Approach of Vectorizing Shopping Paths Sensed by RFID Tags to Classify Retail Customers and its Application with Principal Component Regression

著者名: 稲元　勉（神戸大学大学院システム情報学研究科），大野　麻子（大阪産業大学工学部通信工学科），村尾　元（神戸大学大学院国際文化学研究科）

著者名(英語): Tsutomu Inamoto (Graduate School of System Informatics, Kobe University), Asako Ohno (Department of Electronics, Osaka Sangyo University), Hajime Murao (Graduate School of Intercultural Studies, Kobe University)

キーワード: 分類，小売店，買い物経路，RFID，主成分回帰　　classification，retail store，shopping path，RFID，principal component regression

要約(英語): In this paper, we propose an approach for classifying customers in retail stores into given types according to their shopping paths, each of which is a sequence of sections visited by the corresponding customer and is gathered by an RFID tag. The approach vectorizes a sequence of sections; that is, the approach splits such a sequence into tuples of sections, then sums up occurring counts of those tuples. This vectorization is based on the hypothesis that a customer's type has relation to sub-sequences of sections in his/her shopping path and a conjecture that customers' types can be attributed to co-occurrences of such sub-sequences. After vectorization, the proposed approach applies a general discrimination method to such vectors of equal length.In computational illustrations, the principal component regression is selected as a representative of general discrimination methods and is applied to shopping paths collected in an existing retail store so as to predict whether a customer purchases items much than average or not. Computational results display the effectiveness of the proposed approach as higher forecast accuracies than known works.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.12 （2012） 特集：電気関係学会東海支部連合大会

本誌掲載ページ: 2051-2058 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/132/12/132_2051/_article/-char/ja/