Multistakeholder recommendation with provider constraints

Özge Sürer, Robin Burke, Edward C. Malthouse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations


Recommender systems are typically designed to optimize the utility of the end user. In many settings, however, the end user is not the only stakeholder and this exclusive focus may produce unsatisfactory results for other stakeholders. One such setting is found in multisided platforms, which bring together buyers and sellers. In such platforms, it may be necessary to jointly optimize the value for both buyers and sellers. This paper proposes a constraint-based integer programming optimization model, in which different sets of constraints are used to reflect the goals of the different stakeholders. This model is applied as a post-processing step, so it can easily be added onto an existing recommendation system to make it multistakeholder aware. For computational tractability with larger data sets, we reformulate the integer problem using the Lagrangian dual and use subgradient optimization. In experiments with two data sets, we evaluate empirically the interaction between the utilities of buyers and sellers and show that our approximation can achieve good upper and lower bounds in practical situations.

Original languageEnglish (US)
Title of host publicationRecSys 2018 - 12th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450359016
StatePublished - Sep 27 2018
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: Oct 2 2018Oct 7 2018

Publication series

NameRecSys 2018 - 12th ACM Conference on Recommender Systems


Other12th ACM Conference on Recommender Systems, RecSys 2018


  • Constraint-based Recommendation
  • Multisided Platforms
  • Multistakeholder Recommendation

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software


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