Purchasing Behavior Analysis Model that Considers the Relationship Between Topic Hierarchy and Item Categories

Yuta Sakai*, Yui Matsuoka, Masayuki Goto

*Corresponding author for this work

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

Abstract

With the spread of EC sites, it has become an important work for companies to analyze user preferences contained in accumulated purchase history data and utilize them in marketing measures. A topic model is well known as a method for analyzing user preferences from purchase history data, and a model assuming hierarchy of topics has been proposed as an extension method. The previously proposed PAM (Pachinko Allocation Model) is a highly expressive model in which all upper and lower topics are connected by a network and the relationships between multiple topics can be analyzed. However, PAM is easily affected by the initial values of learning parameters, and it is difficult to obtain stable topics, so the interpretation of the estimated topics becomes unstable. It is dangerous to make business decisions based on the interpretation of such unstable results. Therefore, in this research, instead of using the hierarchy of topics estimated based on the user’s purchasing behavior, we use information with a hierarchical structure of “product categories” given by the EC site for managing items. Therefore, we propose a method that is useful for studying measures and that enables hierarchical topic analysis. Finally, the proposed method is applied to the evaluation history data of the actual EC site to analyze the user’s preference and show its usefulness.

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
Pages344-358
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

  • EC site
  • Purchase history data
  • Topic model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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