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Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve

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Social Computing and Social Media: Applications in Education and Commerce (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13316))

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Abstract

Purchasing actions on e-commerce sites have become very common for general consumers in recent years. Products that were used to be bought at offline shops are purchased are also handled. Such products, like gifts or durable consumer goods, are often purchased infrequently and whose prefer items change each time they are purchased. A lot of methods are proposed for analysis purchase history data in order to improve customer satisfaction. However, most of them focus on the co-occurrence relationship between customers and products and treat products purchased by the same customer as similar. Then, it is difficult to use the conventional product analysis methods that have been proposed for purchase history data is difficult for some kinds of data mentioned before.

Therefore, the authors have proposed an analysis method with extracting features of products by using the coefficients of binary classifiers that discriminates product purchases or not. In this study, we conduct experiments with artificial data in order to evaluate our method. Specifically, we verify how accurately the coefficients can be estimated and under what circumstances they can be estimated more accurately.

This work was supported by JSPS KAKENHI Grant Number 21H04600.

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Authors and Affiliations

  1. School of Creative Science and Engineering of Waseda University, Okubo Shinjuku 3-4-1, Tokyo, Japan

    Ayako Yamagiwa

  2. Waseda University, Okubo Shinjuku 3-4-1, Tokyo, Japan

    Masayuki Goto

Authors
  1. Ayako Yamagiwa
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  2. Masayuki Goto
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Corresponding author

Correspondence to Ayako Yamagiwa .

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Editors and Affiliations

  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Yamagiwa, A., Goto, M. (2022). Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve. In: Meiselwitz, G. (eds) Social Computing and Social Media: Applications in Education and Commerce. HCII 2022. Lecture Notes in Computer Science, vol 13316. Springer, Cham. https://doi.org/10.1007/978-3-031-05064-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-05064-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05063-3

  • Online ISBN: 978-3-031-05064-0

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