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@inproceedings{1802046,
author = {Blanco Sánchez, José Miguel and Ge, Mouzhi and Pitner, Tomáš},
address = {Setúbal, Portugal},
booktitle = {Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
doi = {http://dx.doi.org/10.5220/0010653600003058},
keywords = {Recommender Systems; Recommendation Recovery; Adaptive Filter; User-oriented Recommendation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Setúbal, Portugal},
isbn = {978-989-758-536-4},
pages = {283-290},
publisher = {SciTePress/INSTICC},
title = {Recommendation Recovery with Adaptive Filter for Recommender Systems},
year = {2021}
}
TY - JOUR
ID - 1802046
AU - Blanco Sánchez, José Miguel - Ge, Mouzhi - Pitner, Tomáš
PY - 2021
TI - Recommendation Recovery with Adaptive Filter for Recommender Systems
PB - SciTePress/INSTICC
CY - Setúbal, Portugal
SN - 9789897585364
KW - Recommender Systems
KW - Recommendation Recovery
KW - Adaptive Filter
KW - User-oriented Recommendation
N2 - 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.
ER -
BLANCO SÁNCHEZ, José Miguel, Mouzhi GE a Tomáš PITNER. Recommendation Recovery with Adaptive Filter for Recommender Systems. Online. In \textit{Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST}. Setúbal, Portugal: SciTePress/INSTICC, 2021, s.~283-290. ISBN~978-989-758-536-4. Dostupné z: https://dx.doi.org/10.5220/0010653600003058.
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