Recommendation Recovery with Adaptive Filter for Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00122770" target="_blank" >RIV/00216224:14330/21:00122770 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0010653600003058" target="_blank" >http://dx.doi.org/10.5220/0010653600003058</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010653600003058" target="_blank" >10.5220/0010653600003058</a>
Alternative languages
Result language
angličtina
Original language name
Recommendation Recovery with Adaptive Filter for Recommender Systems
Original language description
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.
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
ISBN
9789897585364
ISSN
2184-3252
e-ISSN
—
Number of pages
8
Pages from-to
283-290
Publisher name
SciTePress/INSTICC
Place of publication
Setúbal, Portugal
Event location
Setúbal, Portugal
Event date
Jan 1, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
000795868100028