Workshop on Context-Aware Recommender Systems

Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Contextual information has been widely recognized as an important modeling dimension both 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 2020 workshop provides a venue for presenting and discussing approaches for 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 online environments.

Original languageEnglish (US)
Title of host publicationRecSys 2020 - 14th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages635-637
Number of pages3
ISBN (Electronic)9781450375832
DOIs
StatePublished - Sep 22 2020
Event14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, Brazil
Duration: Sep 22 2020Sep 26 2020

Publication series

NameRecSys 2020 - 14th ACM Conference on Recommender Systems

Conference

Conference14th ACM Conference on Recommender Systems, RecSys 2020
Country/TerritoryBrazil
CityVirtual, Online
Period9/22/209/26/20

Bibliographical note

Publisher Copyright:
© 2020 Owner/Author.

Keywords

  • Context
  • Context-Aware Recommendation
  • Contextual Modeling
  • Sequence-Aware Recommendation

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