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

Ryosuke Konishi, Fumito Nakamura, Yasushi Kiyoki

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルInformation Modelling and Knowledge Bases XXXI
編集者Ajantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
出版者IOS Press
ページ205-221
ページ数17
ISBN(電子版)9781643680446
DOI
出版物ステータスPublished - 2019 12 13
イベント29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
継続期間: 2019 6 32019 6 7

出版物シリーズ

名前Frontiers in Artificial Intelligence and Applications
321
ISSN(印刷物)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
Finland
Lappeenranta
期間19/6/319/6/7

ASJC Scopus subject areas

  • Artificial Intelligence

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  • これを引用

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