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@inproceedings{2392319,
author = {Blanco Sánchez, José Miguel and Ge, Mouzhi and Pitner, Tomáš},
address = {CHAM},
booktitle = {WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021},
doi = {http://dx.doi.org/10.1007/978-3-031-24197-0_7},
editor = {978-3-031-24197-0},
keywords = {Recommender systems; Recommendation recovery; Adaptive filter; User-oriented recommendation},
howpublished = {elektronická verze "online"},
language = {eng},
location = {CHAM},
isbn = {978-3-031-24196-3},
pages = {107-121},
publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
title = {An Adaptive Filter for Preference Fine-Tuning in Recommender Systems},
year = {2023}
}
TY - JOUR
ID - 2392319
AU - Blanco Sánchez, José Miguel - Ge, Mouzhi - Pitner, Tomáš
PY - 2023
TI - An Adaptive Filter for Preference Fine-Tuning in Recommender Systems
PB - SPRINGER INTERNATIONAL PUBLISHING AG
CY - CHAM
SN - 9783031241963
KW - Recommender systems
KW - Recommendation recovery
KW - Adaptive filter
KW - User-oriented recommendation
N2 - A recommender system may recommend certain items that the users would not prefer. This can be caused by either the imperfection of the recommender system or the change of user preferences. When those failed recommendations appear often in the system, the users may consider that the recommender system is not able to capture the user preference. This can result in abandoning to further use the recommender system. However, given the possible failed recommendations, most recommender systems will ignore the non-preferred recommendations. Therefore, this paper proposes failure recovery solution for recommender systems with an adaptive filter. On the one hand, the proposed solution can deal with the failed recommendations while keeping the user engagement. Additionally, it allows the recommender system to dynamically fine tune the preferred items and become a long-term application. Also, the adaptive filter can avoid the cost of constantly updating the recommender learning model.
ER -
BLANCO SÁNCHEZ, José Miguel, Mouzhi GE a Tomáš PITNER. An Adaptive Filter for Preference Fine-Tuning in Recommender Systems. Online. In 978-3-031-24197-0. \textit{WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021}. CHAM: SPRINGER INTERNATIONAL PUBLISHING AG, 2023, s.~107-121. ISBN~978-3-031-24196-3. Dostupné z: https://dx.doi.org/10.1007/978-3-031-24197-0\_{}7.
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