Fairness-preserving Group Recommendations With User Weighting
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10432905" target="_blank" >RIV/00216208:11320/21:10432905 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1145/3450614.3461679" target="_blank" >https://doi.org/10.1145/3450614.3461679</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3450614.3461679" target="_blank" >10.1145/3450614.3461679</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Fairness-preserving Group Recommendations With User Weighting
Popis výsledku v původním jazyce
Group recommendations are an extension of "single-user" personalized recommender systems (RS), where the final recommendations should comply with preferences of several group members. An important challenge in group RS is the problem of fairness, i.e., no user's preferences should be largely ignored by the RS. Traditional strategies, such as "least misery" or "average rating", tackle the problem of fairness, but they resolve it separately for each item. This may cause a systematic bias against some group members. In contrast, this paper considers both fairness and relevance as a rank-sensitive list property. We propose EP-FuzzDA algorithm that utilizes an optimization criterion encapsulating both fairness and relevance. In conducted experiments, EP-FuzzDA outperforms several state-of-the-art baselines. Another advantage of EP-FuzzDA is the capability to adjust on non-uniform importance of group members enabling e.g. to maintain the long-term fairness across several recommending sessions.
Název v anglickém jazyce
Fairness-preserving Group Recommendations With User Weighting
Popis výsledku anglicky
Group recommendations are an extension of "single-user" personalized recommender systems (RS), where the final recommendations should comply with preferences of several group members. An important challenge in group RS is the problem of fairness, i.e., no user's preferences should be largely ignored by the RS. Traditional strategies, such as "least misery" or "average rating", tackle the problem of fairness, but they resolve it separately for each item. This may cause a systematic bias against some group members. In contrast, this paper considers both fairness and relevance as a rank-sensitive list property. We propose EP-FuzzDA algorithm that utilizes an optimization criterion encapsulating both fairness and relevance. In conducted experiments, EP-FuzzDA outperforms several state-of-the-art baselines. Another advantage of EP-FuzzDA is the capability to adjust on non-uniform importance of group members enabling e.g. to maintain the long-term fairness across several recommending sessions.
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
2021
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
Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21 Adjunct)
ISBN
978-1-4503-8367-7
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
4-9
Název nakladatele
Association for Computing Machinery
Místo vydání
New York, NY, USA
Místo konání akce
Utrecht, Netherlands
Datum konání akce
21. 6. 2021
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—