Workshop on Context-Aware Recommender Systems (CARS) 2024

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

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Publication Details

Published
2024-10-08
DOI
https://doi.org/10.1145/3640457.3687102
Citations
21
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
1.56
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article

Workshop on Context-Aware Recommender Systems (CARS) 2024

Moshe Unger, Alexander Tuzhilin, Konstantin Bauman, Gediminas Adomavičius et al.
21 citations
Recommender Systems and Techniques
1.56
article

Workshop on Context-Aware Recommender Systems (CARS) 2024

Moshe Unger, Alexander Tuzhilin, Konstantin Bauman, Gediminas Adomavičius, Bamshad Mobasher
article en
21 citations

Abstract

Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

DePaul University (US), University of Minnesota (US), Tel Aviv University (IL), College of Management Academic Studies (IL), New York University (US), Temple University (US)
Openalex Percentile: Top 12%
Recommender Systems and Techniques
1.56
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