An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

An Adaptive Filter for Preference Fine-Tuning in Recommender Systems

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

WEB INFORMATION SYSTEMS AND TECHNOLOGIES, WEBIST 2020, WEBIST 2021

ISBN

9783031241963

ISSN

1865-1348

e-ISSN

—

Počet stran výsledku

15

Strana od-do

107-121

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

CHAM

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000972038800007