Workshop on Recommendation in Multistakeholder Environments

One of the most essential aspects of any recommender system is personalization—how well the
recommendations delivered suit the user’s interests. However, in many real world applications, there are other stakeholders whose needs and interests should be taken into account. In multisided e-commerce platforms, such as auction sites, there are parties on both sides of the recommendation interaction whose perspectives should be considered. There are also contexts in which the recommender system itself also has certain objectives that should be incorporated into the recommendation generation. Fairness-aware recommendation, long-tail promotion, and profit
maximization are all examples of objectives that may be important in different applications. In such multistakeholder environments, the recommender system will need to balance the (possibly
conflicting) interests of different parties.

  • Robin Burke, University of Colorado, Boulder, USA
  • Himan Abdollahpouri, University of Colorado, Boulder
  • Ed Malthouse, Northwestern University, USA
  • KP Thai, Squirrel AI Learning, China
  • Yongfeng Zhang, Rutgers, the State University of New Jersey, USA


Friday, Sept 20, 2019, 09:00-17:30


Room 0.1

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