Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10422758" target="_blank" >RIV/00216208:11320/20:10422758 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1145/3428757.3429105" target="_blank" >https://doi.org/10.1145/3428757.3429105</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3428757.3429105" target="_blank" >10.1145/3428757.3429105</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations
Popis výsledku v původním jazyce
In this paper, we focus on the problems of fair aggregation of recommender systems (RS) and over-exposure of users with insignificant recommendations. While fair aggregation of diverse RS may contribute to both calibration and diversity challenges, some recently proposed methods suffer from repeating the same set of recommendations to the user over and over again. However, it may be difficult to distinguish between situations when users ignore recommendations because they are irrelevant or because they did not notice them. In order to cope with these challenges, we propose an innovative off-line RS evaluation methodology based on the noticeability of recommended items. We further propose a Fuzzy D'Hondt's algorithm with personalized implicit negative feedback attribution (FDHondtINF). The algorithm is designed to provide a fair ordering of candidate items coming from multiple individual RS, while considering also the objects previously ignored by the current user. FDHondtINF was evaluated off-line along with other aggregation methods and individual RS on MovieLens 1M dataset. The algorithm performs especially well in situations when the recommended items are less noticeable, or when a sequence of multiple recommendations for the same user model is given.
Název v anglickém jazyce
Personalized Implicit Negative Feedback Enhancements for Fuzzy D'Hondt's Recommendation Aggregations
Popis výsledku anglicky
In this paper, we focus on the problems of fair aggregation of recommender systems (RS) and over-exposure of users with insignificant recommendations. While fair aggregation of diverse RS may contribute to both calibration and diversity challenges, some recently proposed methods suffer from repeating the same set of recommendations to the user over and over again. However, it may be difficult to distinguish between situations when users ignore recommendations because they are irrelevant or because they did not notice them. In order to cope with these challenges, we propose an innovative off-line RS evaluation methodology based on the noticeability of recommended items. We further propose a Fuzzy D'Hondt's algorithm with personalized implicit negative feedback attribution (FDHondtINF). The algorithm is designed to provide a fair ordering of candidate items coming from multiple individual RS, while considering also the objects previously ignored by the current user. FDHondtINF was evaluated off-line along with other aggregation methods and individual RS on MovieLens 1M dataset. The algorithm performs especially well in situations when the recommended items are less noticeable, or when a sequence of multiple recommendations for the same user model is given.
Klasifikace
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)
Návaznosti výsledku
Projekt
<a href="/cs/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexibilní modely pro hledání známé scény v rozsáhlých kolekcích videa</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
ISBN
978-1-4503-8922-8
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
210-215
Název nakladatele
ACM
Místo vydání
New York, USA
Místo konání akce
Chiang Mai Thailand
Datum konání akce
30. 11. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—