Family structure attribute estimation method for product recommendation system

Chiaki Doi, Masaji Katagiri, Takashi Araki, Daizo Ikeda, Hiroshi Shigeno

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

Abstract

This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning of smart recommendations to the consumer family such as suggesting products that reflectthe family structure attributes of the consumer.

Original languageEnglish
Title of host publicationProceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages167-173
Number of pages7
ISBN (Electronic)9781509060283
DOIs
Publication statusPublished - 2017 May 5
Event31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 - Taipei, Taiwan, Province of China
Duration: 2017 Mar 272017 Mar 29

Other

Other31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
CountryTaiwan, Province of China
CityTaipei
Period17/3/2717/3/29

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Keywords

  • Family Structure Estimation
  • Purchasing Data

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Doi, C., Katagiri, M., Araki, T., Ikeda, D., & Shigeno, H. (2017). Family structure attribute estimation method for product recommendation system. In Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017 (pp. 167-173). [7920905] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AINA.2017.12