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.
Authors
- Moshe Unger (ORCID: https://orcid.org/0000-0001-5512-0331)
- Alexander Tuzhilin (ORCID: https://orcid.org/0000-0003-3354-8462)
- Konstantin Bauman (ORCID: https://orcid.org/0000-0003-4964-9882)
- Gediminas Adomavičius (ORCID: https://orcid.org/0000-0001-5251-5098)
- Bamshad Mobasher (ORCID: https://orcid.org/0000-0001-9701-9178)
Institutions
- DePaul University (US)
- University of Minnesota (US)
- Tel Aviv University (IL)
- College of Management Academic Studies (IL)
- New York University (US)
- Temple University (US)
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