An Adaptive Filter for Preference Fine-Tuning in Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00133947" target="_blank" >RIV/00216224:14330/23:00133947 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-24197-0_7" target="_blank" >http://dx.doi.org/10.1007/978-3-031-24197-0_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-24197-0_7" target="_blank" >10.1007/978-3-031-24197-0_7</a>
Alternative languages
Result language
angličtina
Original language name
An Adaptive Filter for Preference Fine-Tuning in Recommender Systems
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021
ISBN
9783031241963
ISSN
1865-1348
e-ISSN
—
Number of pages
15
Pages from-to
107-121
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
CHAM
Event date
Jan 1, 2023
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000972038800007