On-the-spot Knowledge Refinement for an Interactive Recommender System

Yuichiro Ikemoto, Kazuhiro Kuwabara

2019

Abstract

This paper proposes a method to refine knowledge about items in an item database for an interactive recommender system. The proposed method is integrated into a recommender system and invoked when the system recognizes a problem with the item database from users’ feedback about recommended items. The proposed method collects information from a user via similar interactions to those of a recommendation process. In this way, a user who is knowledgeable in a target domain, but does not necessarily know the internal system can participate in the knowledge refinement process. Thus, the proposed method paves the way for applying crowdsourcing to knowledge refinement.

Paper Citation

in Harvard Style

Ikemoto Y. and Kuwabara K. (2019). On-the-spot Knowledge Refinement for an Interactive Recommender System.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 817-823. DOI: 10.5220/0007571508170823

in Bibtex Style

@conference{icaart19,
author={Yuichiro Ikemoto and Kazuhiro Kuwabara},
title={On-the-spot Knowledge Refinement for an Interactive Recommender System},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={817-823},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007571508170823},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - On-the-spot Knowledge Refinement for an Interactive Recommender System
SN - 978-989-758-350-6
AU - Ikemoto Y.
AU - Kuwabara K.
PY - 2019
SP - 817
EP - 823
DO - 10.5220/0007571508170823