Recommendation Recovery with Adaptive Filter for Recommender Systems

José Blanco, Mouzhi Ge, Tomáš Pitner

2021

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.

Paper Citation

in Harvard Style

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 - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 283-290. DOI: 10.5220/0010653600003058

in Bibtex Style

@conference{webist21,
author={José 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 - Volume 1: WEBIST,},
year={2021},
pages={283-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010653600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Recommendation Recovery with Adaptive Filter for Recommender Systems
SN - 978-989-758-536-4
AU - Blanco J.
AU - Ge M.
AU - Pitner T.
PY - 2021
SP - 283
EP - 290
DO - 10.5220/0010653600003058