Recommendation Recovery with Adaptive Filter for Recommender Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Recommendation Recovery with Adaptive Filter for Recommender Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Recommendation Recovery with Adaptive Filter for Recommender Systems

Popis výsledku anglicky

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.

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

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST

ISBN

9789897585364

ISSN

2184-3252

e-ISSN

—

Počet stran výsledku

8

Strana od-do

283-290

Název nakladatele

SciTePress/INSTICC

Místo vydání

Setúbal, Portugal

Místo konání akce

Setúbal, Portugal

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000795868100028