A constrained optimization approach for calibrated recommendations

Sinan Seymen, Himan Abdollahpouri, Edward C. Malthouse

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

8 Scopus citations


In recommender systems (RS) it is important to ensure that the various (past) areas of interest of a user are reflected with their corresponding proportions in the recommendation lists. In other words, when a user has watched, say, 60 romance movies and 40 Comedy movies, then it is reasonable to expect the personalized list of recommended movies to contain about 60% romance and 40% comedy movies as well. This property is known as calibration, and it has recently received much attention in the RS community. Greedy heuristic approaches have been proposed to calibrate recommendations, and although they provide great improvements, they can result in inefficient solutions in that a better one can be missed because of the myopic nature of these algorithms. This paper addresses the calibration problem from a constrained optimization perspective and provides a model to combine both accuracy and calibration. Experimental results show that our approach outperforms the state-of-the-art heuristics for calibration in most cases on both accuracy of the recommendations and the level of calibrations the recommendation lists achieve. We give a small example to illustrate why the heuristic fails to find the optimal solution.

Original languageEnglish (US)
Title of host publicationRecSys 2021 - 15th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450384582
StatePublished - Sep 13 2021
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: Sep 27 2021Oct 1 2021

Publication series

NameRecSys 2021 - 15th ACM Conference on Recommender Systems


Conference15th ACM Conference on Recommender Systems, RecSys 2021
CityVirtual, Online


  • Calibration
  • Multi-objective
  • Optimization
  • Recommender systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Networks and Communications
  • Hardware and Architecture


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