Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingCommunication

3 Citations (Scopus)
177 Downloads (Pure)

Abstract

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
Pages380-381
ISBN (Electronic)978-1-4503-4652-8
DOIs
Publication statusPublished - 31 Aug 2017
EventRecSys 2017: 11th ACM Conference on Recommender Systems - Como, Italy, Como, Italy
Duration: 27 Aug 201731 Aug 2017
Conference number: 11
https://recsys.acm.org/recsys17/

Conference

ConferenceRecSys 2017: 11th ACM Conference on Recommender Systems
Number11
LocationComo, Italy
CountryItaly
CityComo
Period27/08/201731/08/2017
Internet address

Keywords

  • complex recommendation

Cite this

Bogers, T., Koolen, M., Mobasher, B., Said, A., & Tuzhilin, A. (2017). Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). In RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 380-381). Association for Computing Machinery. https://doi.org/10.1145/3109859.3109958