ComplexRec 2020: Workshop on Recommendation in Complex Environments

Toine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review


During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers 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 '20: Proceedings of the 14th ACM Conference on Recommender Systems
Number of pages2
PublisherAssociation for Computing Machinery
Publication date22 Sep 2020
ISBN (Electronic)9781450375832
Publication statusPublished - 22 Sep 2020
Event14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, Brazil
Duration: 22 Sep 202026 Sep 2020


Conference14th ACM Conference on Recommender Systems, RecSys 2020
CityVirtual, Online
SponsorACM Special Interest Group on Artificial Intelligence (SIGAI), ACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB), Special Interest Group on Information Retrieval (ACM SIGIR), ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD), Special Interest Group on Economics and Computation (SIGecom)
SeriesRecSys 2020 - 14th ACM Conference on Recommender Systems


  • complex recommendation
  • recommender systems


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