Estimating adaptive individual interests and needs based on online local variational inference for a logistic regression mixture model

Ryosuke Konishi, Fumito Nakamura, Yasushi Kiyoki

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

1 Citation (Scopus)

Abstract

In real companies engaged in economic activities through transactions involving consumer items, such as retail, distribution, finance, and information materials, supplying an opportunity to customers to choose specialized items is an important factor that can improve customer satisfaction and convenience allowing their diverse and time-dependent needs to be met. However, capturing the specialized needs of customers accurately is a difficult task because their needs depend on time, context, situation, and meaning. Recently, physical computational environments have been developing rapidly, thereby allowing easy implementation to sense a customer's action and deal with it sequentially. In this paper, we propose a personalized method to predict individual interests and demands appropriately. In particular, the system learns the customers' situation, meaning, and action from their action history, and reflects a feedback of the result to predict the next action. To realize this method, we utilize the following two methodologies: The mathematical model of meaning (MMM), which is a semantic associative search technology; and the local variational inference (LVI), which is an approximation of the Bayesian inference. A numerical experiment shows that the proposed method performed better than a typical method.

Original languageEnglish
Title of host publicationInternational Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Proceedings
EditorsTri Hadiah Muliawati, Muhammad Febrian Ardiansyah, Dewi Mutiara Sari, Desy Intan Permatasari, Mu'arifin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages164-169
Number of pages6
ISBN (Electronic)9781538680797
DOIs
Publication statusPublished - 2019 Jan 28
Event2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Bali, Indonesia
Duration: 2018 Oct 292018 Oct 30

Publication series

NameInternational Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Proceedings

Conference

Conference2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018
CountryIndonesia
CityBali
Period18/10/2918/10/30

Keywords

  • local variational inference
  • mathematical model of meaning
  • Personalized recommendation method
  • semantic associative search

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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  • Cite this

    Konishi, R., Nakamura, F., & Kiyoki, Y. (2019). Estimating adaptive individual interests and needs based on online local variational inference for a logistic regression mixture model. In T. H. Muliawati, M. F. Ardiansyah, D. M. Sari, D. I. Permatasari, & Mu'arifin (Eds.), International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Proceedings (pp. 164-169). [8628512] (International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/KCIC.2018.8628512