Exploiting Performance Estimates for Augmenting Recommendation Ensembles

Gustavo Penha, Rodrygo L.T. Santos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)


Ensembling multiple recommender systems via stacking has shown to be effective at improving collaborative recommendation. Recent work extends stacking to use additional user performance predictors (e.g., the total number of ratings made by the user) to help determine how much each base recommender should contribute to the ensemble. Nonetheless, despite the cost of handcrafting discriminative predictors, which typically requires deep knowledge of the strengths and weaknesses of each recommender in the ensemble, only minor improvements have been observed. To overcome this limitation, instead of engineering complex features to predict the performance of different recommenders for a given user, we propose to directly estimate these performances by leveraging the user's own historical ratings. Experiments on real-world datasets from multiple domains demonstrate that using performance estimates as additional features can significantly improve the accuracy of state-of-the-art ensemblers, achieving nDCG@20 improvements by an average of 23% over not using them.

Original languageEnglish
Title of host publicationRecSys 2020 - 14th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450375832
Publication statusPublished - 2020
Event14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, Brazil
Duration: 22 Sep 202026 Sep 2020

Publication series

NameRecSys 2020 - 14th ACM Conference on Recommender Systems


Conference14th ACM Conference on Recommender Systems, RecSys 2020
CityVirtual, Online


  • Ensembling
  • Learning to Rank
  • Performance Estimation
  • Performance Prediction
  • Recommender Systems


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