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Authors: José Miguel Blanco 1 ; Mouzhi Ge 2 and Tomáš Pitner 1

Affiliations: 1 Faculty of Informatics, Masaryk University, Brno, Czech Republic ; 2 Deggendorf Institute of Technology, Germany

Keyword(s): Recommender Systems, Recommendation Recovery, Adaptive Filter, User-oriented Recommendation.

Abstract: Most recommender systems are focused on suggesting the optimal recommendations rather than finding a way to recover from a failed recommendation. Thus, when a failed recommendation appears several times, users may abandon to use a recommender system by considering that the system does not take her preference into account. One of the reasons is that when a user does not like a recommendation, this preference cannot be instantly captured by the recommender learning model, since the learning model cannot be constantly updated. Although this can be to some extent alleviated by critique-based algorithms, fine tuning the preference is not capable of fully expelling not-preferred items. This paper is therefore to propose a recommender recovery solution with an adaptive filter to deal with the failed recommendations while keeping the user engagement and, in turn, allow the recommender system to become a long-term application. It can also avoid the cost of constantly updating the recommender learning model. (More)

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Paper citation in several formats:
Blanco, J.; Ge, M. and Pitner, T. (2021). Recommendation Recovery with Adaptive Filter for Recommender Systems. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-536-4; ISSN 2184-3252, SciTePress, pages 283-290. DOI: 10.5220/0010653600003058

@conference{webist21,
author={José Miguel Blanco. and Mouzhi Ge. and Tomáš Pitner.},
title={Recommendation Recovery with Adaptive Filter for Recommender Systems},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
year={2021},
pages={283-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010653600003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
TI - Recommendation Recovery with Adaptive Filter for Recommender Systems
SN - 978-989-758-536-4
IS - 2184-3252
AU - Blanco, J.
AU - Ge, M.
AU - Pitner, T.
PY - 2021
SP - 283
EP - 290
DO - 10.5220/0010653600003058
PB - SciTePress

- Science and Technology Publications, Lda.
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