Putting things in context: challenge on context-aware movie recommendation

Alan Said, Shlomo Berkovsky*, Ernesto W. De Luca

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

49 Citations (Scopus)


The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event on Context-Awareness in Recommender Systems at the 2010 ACM Recommender Systems conference. The challenge focused on three context-aware recommendation tasks: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real world online movie recommendation communities and competed against each other for obtaining the highest recommendation accuracy for each task. The datasets contained contextual features, such as mood, plot annotation, social network, and comments, normally not available in movie recommendation datasets. Over 40 teams from 20 countries participated in the challenge. Their participation was summarized by 10 papers accepted to the CAMRa workshop.
Original languageEnglish
Title of host publicationCAMRa '10 Proceedings of the Workshop on Context-Aware Movie Recommendation
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450302586
Publication statusPublished - 2010
Externally publishedYes
EventWorkshop on Context-Aware Movie Recommendation, CAMRa '10 - Barcelona, Spain
Duration: 30 Sep 201030 Sep 2010

Publication series

NameACM International Conference Proceeding Series


ConferenceWorkshop on Context-Aware Movie Recommendation, CAMRa '10


  • Recommender systems
  • context-aware personalization
  • context modeling
  • social networks
  • datasets
  • social network analysis
  • user modeling

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