@article{oai:ipsj.ixsq.nii.ac.jp:00228864,
 author = {安井, 彰悟 and 武藤, 敦子 and 島, 孔介 and 森山, 甲一 and 松井, 藤五郎 and 犬塚, 信博 and Shogo, Yasui and Atsuko, Mutoh and Kosuke, Shima and Koichi, Moriyama and Tohgoroh, Matsui and Nobuhiro, Inuzuka},
 issue = {2},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Oct},
 note = {消費者の購買行動を分析する技術としてPOSデータ分析がある．商品の売り上げ向上のために小売店の売り上げ全体におけるジャンル別の商品売上割合をもとに店舗をクラスタリングし，クラスタ内の店舗で扱う商品や立地等傾向を明らかにする手法がある．しかし，飲食店においては売り上げだけではなく顧客個人の注文方法に傾向がみられる．そこで本研究では，レシートごとの注文から非負値行列因子分解を用いて注文傾向を抽出し店舗の分類を行う手法を提案する．この手法では頻出する注文傾向で店舗を分類するため，店舗のニーズと立地・環境との関連性の特定に活用が可能になる．実験では，実店舗である飲食店のPOSデータに対して本提案手法を適用し有効性を確認した．, POS data analysis is a technique for analyzing consumer purchasing behavior. In order to improve product sales, there is a technique for clustering stores based on the ratio of product sales by genre to total retail store sales, and clarifying the relationship between location and the tendency to order at stores within a cluster. However, in restaurants, the trend is not in the number of product sales, but in the content of individual customer orders. In this study, we propose a method to classify stores by extracting ordering trends from the orders of each receipt using non-negative matrix factorization. Since this method classifies stores based on frequently occurring order trends, it can be used to identify the relationship between store needs and location/environment. In an experiment, we applied the proposed method to POS data of several restaurants and confirmed its effectiveness.},
 pages = {110--117},
 title = {POSデータを用いた注文傾向の抽出による店舗クラスタ分析},
 volume = {16},
 year = {2023}
}