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

Yuichiro Ikemoto, Kazuhiro Kuwabara

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.

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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