ComplexRec: Workshop on Recommendation in Complex Environments

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.

For the past four years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution. For the fifth edition of ComplexRec we aim to narrow the focus of the workshop and contributions to the workshop about topics related to one of the two main themes on complex recommendation: complex inputs and complex outputs.

  • Casper Petersen, SamPension
  • Himan Abdollahpouri, Northwestern University
  • Toine Bogers, Aalborg University Copenhagen
  • Bamshad Mobasher, DePaul University
  • Maria Soledad Pera, Boise State University


Half day.

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