Attribute Analysis of Quick Response Code Payment Users Using Discriminant Non-negative Matrix Factorization
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Attribute Analysis of Quick Response Code Payment Users Using Discriminant Non-negative Matrix Factorization

Authors: Hironori Karachi, Haruka Yamashita


Recently, the system of quick response (QR) code is getting popular. Many companies introduce new QR code payment services and the services are competing with each other to increase the number of users. For increasing the number of users, we should grasp the difference of feature of the demographic information, usage information, and value of users between services. In this study, we conduct an analysis of real-world data provided by Nomura Research Institute including the demographic data of users and information of users’ usages of two services; LINE Pay, and PayPay. For analyzing such data and interpret the feature of them, Nonnegative Matrix Factorization (NMF) is widely used; however, in case of the target data, there is a problem of the missing data. EM-algorithm NMF (EMNMF) to complete unknown values for understanding the feature of the given data presented by matrix shape. Moreover, for comparing the result of the NMF analysis of two matrices, there is Discriminant NMF (DNMF) shows the difference of users features between two matrices. In this study, we combine EMNMF and DNMF and also analyze the target data. As the interpretation, we show the difference of the features of users between LINE Pay and Paypay.

Keywords: Data science, non-negative matrix factorization, missing data, quality of services.

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[1] Chen, Tietie; Yoko Ishino, ‘Study on Popularization of QR Code Settlement in Japan.’ Agents and Multi-Agent Systems: Technologies and Applications 2019. Springer, Singapore, 2020. 297-307.
[2] Berry, Michael W., et al. Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics & Data Analysis, 2007, 52.1: 155-173.
[3] Pauca, V. Paul, Jon Piper; Robert J. Plemmons. Nonnegative matrix factorization for spectral data analysis. Linear Algebra & its Applications, 2006, 416.1: 29-47.
[4] Zhang, Sheng, et al. Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2006. 549-553.
[5] Zafeiriou, Stefanos, et al. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Transactions on Neural Networks, 2006, 17.3: 683-695.
[6] Ano, Tsubasa, Haruka Yamashita; Masayuki Goto. An analysis of Consumer Panel Data Based on the Discriminant Nonnegative Matrix Factorization, Proceedings of the APIEMS2018, 2018. CD-included.
[7] Home page of PayPay Corporation,, last browsing: Feb 22, 2021.
[8] Com. Home page of LINE Corporation, https://LINEPaycorp/ja/, last browsing: Feb 22, 2021.
[9] Home page of Nomura Research Institute, Written in Japanese,, last browsing: 22nd Feb 2021.
[10] Ning, Shangbin; Fengchao Zuo. Sparsity-constrained NMF algorithm based on evolution strategy for hyperspectral unmixing. In: MATEC Web of Conferences. EDP Sciences, 2018, 232: 04019.
[11] Yang, Zhirong, et al. Kullback-Leibler divergence for nonnegative matrix factorization. In: Lecture Notes in Computer Science International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 2011: 250-257.
[12] Nikitidis, Symeon, et al. Subclass discriminant nonnegative matrix factorization for facial image analysis. Pattern Recognition, 2012, 45.12: 4080-4091.