Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsCommunication


Recommendation algorithms for ratings prediction and item ranking have steadily matured during the past decade. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on these complex recommendation scenarios. The ComplexRec 2017 workshop addressed this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all-solution.
Original languageEnglish
Title of host publicationRecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems
Number of pages2
PublisherAssociation for Computing Machinery
Publication date31 Aug 2017
ISBN (Electronic)978-1-4503-4652-8
Publication statusPublished - 31 Aug 2017
Externally publishedYes
EventRecSys 2017: 11th ACM Conference on Recommender Systems - Como, Italy, Como, Italy
Duration: 27 Aug 201731 Aug 2017
Conference number: 11


ConferenceRecSys 2017: 11th ACM Conference on Recommender Systems
LocationComo, Italy
Internet address


  • Recommendation algorithms
  • Complex recommendation scenarios
  • User background needs
  • Ratings prediction
  • Item ranking


Dive into the research topics of 'Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)'. Together they form a unique fingerprint.

Cite this