Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model

Ryosuke Konishi, Fumito Nakamura, Yasushi Kiyoki

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

Abstract

Improved computing environments performing large-scale data processing and high-speed computational processing facilitate the delivery of new algorithms to businesses while considering cost efficiency for small-scale investments. Implementing the proposed method more as a criterion for feasibility and economic rationality in specific problem areas rather than as an approach to generic issues, we aim to develop technologies of practical use in the real world. Recently, it has become possible for customers to monitor their buying behavior through smart devices, and with the improvement of computing performance, it has become possible to improve the accuracy of prediction and recommendation cycles through active online learning. This study proposes a method for dynamically recommending products that are highly likely to be selected by the user by combining the user's reaction with reuse of knowledge and real-time online learning to cyclically repeat feedback that is more specific to the user.We propose a method to sense streaming data by utilizing a user's behavior, intervening a user's behavioral change through interactions, such as recommendations, and evaluating the user's buying intention and interest in each product. Using the evaluation results for recommendations helps achieve positive feedback and effectively support the selection of more exciting or different products. We propose a recommendation method specific to individual customers based on past transaction data, where changes can be monitored in real-time by reusing the knowledge acquired in advance through batch processing of knowledge discovery and data mining and processing the stream data in real-time online. We will present the implementation of our proposed method targeting the database system and machine learning algorithm.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXXI
EditorsAjantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
PublisherIOS Press
Pages205-221
Number of pages17
ISBN (Electronic)9781643680446
DOIs
Publication statusPublished - 2019 Dec 13
Event29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
Duration: 2019 Jun 32019 Jun 7

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume321
ISSN (Print)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
CountryFinland
CityLappeenranta
Period19/6/319/6/7

    Fingerprint

Keywords

  • Local variational inference
  • Logistic regression mixture model
  • Mathematical model of meaning
  • Recommendation system
  • Retail application

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Konishi, R., Nakamura, F., & Kiyoki, Y. (2019). Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model. In A. Dahanayake, J. Huiskonen, Y. Kiyoki, B. Thalheim, H. Jaakkola, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXXI (pp. 205-221). (Frontiers in Artificial Intelligence and Applications; Vol. 321). IOS Press. https://doi.org/10.3233/FAIA200016