Fairness-preserving Group Recommendations With User Weighting

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Fairness-preserving Group Recommendations With User Weighting
Original language description
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.
Czech name
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Czech description
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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)

Project
<a href="/en/project/GJ19-22071Y" target="_blank" >GJ19-22071Y: Flexible models for known-item search in large video collections</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21 Adjunct)
ISBN
978-1-4503-8367-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
4-9
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, USA
Event location
Utrecht, Netherlands
Event date
Jun 21, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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