Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
ISBN
978-1-4503-9232-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
302-307
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, United States
Event location
Barcelona, Spain
Event date
Jul 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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