Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10447497" target="_blank" >RIV/00216208:11320/22:10447497 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1145/3511047.3537650" target="_blank" >https://doi.org/10.1145/3511047.3537650</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3511047.3537650" target="_blank" >10.1145/3511047.3537650</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
Popis výsledku v původním jazyce
Group recommendations are a specific case of recommender systems (RS), where instead of recommending for each individual independently, shared recommendations are produced for groups of users. Usually, group recommendation techniques (i.e., group aggregators) are built on top of common "single-user"RS and the resulting group recommendation should reflect both the overall utility of the recommendation as well as fairness among the utilities of individual group members. Off-line evaluations of group recommendations were so far resolved either as a tightly coupled pair with the underlying RS or in a decoupled fashion. In the latter case, the relevance scores estimated by underlying RS serves as a ground truth for the evaluation of group aggregators. Both coupled and decoupled evaluation may suffer from different biases that provide illicit advantages to some classes of group recommending strategies. In this paper, we focus on the decoupled evaluation protocol and possible polarity bias of the underlying RS. We define polarity bias as situations when RS either locally or globally under-estimate or over-estimate the true user preferences. We propose several polarity de-biasing strategies and in the experimental part, we focus on the capability of group aggregation strategies to cope with the polarity biased input data.
Název v anglickém jazyce
Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
Popis výsledku anglicky
Group recommendations are a specific case of recommender systems (RS), where instead of recommending for each individual independently, shared recommendations are produced for groups of users. Usually, group recommendation techniques (i.e., group aggregators) are built on top of common "single-user"RS and the resulting group recommendation should reflect both the overall utility of the recommendation as well as fairness among the utilities of individual group members. Off-line evaluations of group recommendations were so far resolved either as a tightly coupled pair with the underlying RS or in a decoupled fashion. In the latter case, the relevance scores estimated by underlying RS serves as a ground truth for the evaluation of group aggregators. Both coupled and decoupled evaluation may suffer from different biases that provide illicit advantages to some classes of group recommending strategies. In this paper, we focus on the decoupled evaluation protocol and possible polarity bias of the underlying RS. We define polarity bias as situations when RS either locally or globally under-estimate or over-estimate the true user preferences. We propose several polarity de-biasing strategies and in the experimental part, we focus on the capability of group aggregation strategies to cope with the polarity biased input data.
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/GA22-21696S" target="_blank" >GA22-21696S: Hluboké vizuální reprezentace nestrukturovaných dat</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
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
UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
ISBN
978-1-4503-9232-7
ISSN
—
e-ISSN
—
Počet stran výsledku
6
Strana od-do
302-307
Název nakladatele
Association for Computing Machinery
Místo vydání
New York, NY, United States
Místo konání akce
Barcelona, Spain
Datum konání akce
4. 7. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—