Tutorial on Offline Evaluation for Group Recommender Systems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10448370" target="_blank" >RIV/00216208:11320/22:10448370 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3523227.3547371" target="_blank" >https://doi.org/10.1145/3523227.3547371</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3523227.3547371" target="_blank" >10.1145/3523227.3547371</a>

Result language
angličtina
Original language name
Tutorial on Offline Evaluation for Group Recommender Systems
Original language description
Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people. Clearly, many activities are often experienced by a group rather than an individual (visiting a restaurant, traveling, watching a movie, etc.) hence the requirement for such systems. The topic is gradually receiving more and more attention, with an increased number of papers published at significant venues, which is enabled by the predominance of online social platforms that allow their users to interact in groups, as well as to plan group activities. However, the research area lacks certain ground rules, such as basic evaluation agreements. We believe this is one of the main obstacles to make advances in the research area, and to enable researchers to compare and continue each others' works. In other words, setting the basic evaluation agreements is a stepping-stone towards reproducible Group Recommenders research. The goal of this tutorial is to tackle this problem, by providing the basic principles of the GRSs offline evaluation approaches.
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/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)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
ISBN
978-1-4503-9278-5
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
702-705
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Seattle WA, USA
Event date
Sep 18, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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