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

Ayako Yamagiwa*, Masayuki Goto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationSocial Computing and Social Media
Subtitle of host publicationApplications in Education and Commerce - 14th International Conference, SCSM 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
EditorsGabriele Meiselwitz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages388-402
Number of pages15
ISBN (Print)9783031050633
DOIs
Publication statusPublished - 2022
Event14th International Conference on Social Computing and Social Media, SCSM 2022 Held as Part of the 24th HCI International Conference, HCII 2022 - Virtual, Online
Duration: 2022 Jun 262022 Jul 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13316 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Social Computing and Social Media, SCSM 2022 Held as Part of the 24th HCI International Conference, HCII 2022
CityVirtual, Online
Period22/6/2622/7/1

Keywords

  • Binary classifiers
  • Feature embedding
  • Product analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Evaluation of Analysis Model for Products with Coefficients of Binary Classifiers and Consideration of Way to Improve'. Together they form a unique fingerprint.

Cite this