Retargeted matrix factorization for collaborative filtering

Oluwasanmi Koyejo, Sreangsu Acharyya, Joydeep Ghosh

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


This paper introduces retargeted matrix factorization (R-MF); a novel approach for learning the user-wise ranking of items in the context of collaborative filtering. R-MF learns to rank by "retargeting" the item ratings of each user, searching for a monotonic transformation of the ratings that results in a better fit while preserving the ranked order of each user's ratings. The retargeting is combined with an underlying matrix factorization regression model that couples the user-wise rankings to exploit shared low dimensional structure. We show that R-MF recovers a unique solution under mild conditions, and propose a simple and efficient optimization scheme that alternates between retargeting the ratings subject to ordering constraints, and matrix factorization regression. The retargeting step is independent for each user, and is trivially parallelized. The ranking performance of retargeted matrix factorization is evaluated on benchmark movie recommendation datasets and results in superior ranking performance compared to collaborative filtering algorithms specifically designed to optimize ranking metrics.

Original languageEnglish (US)
Title of host publicationRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
Number of pages8
StatePublished - 2013
Externally publishedYes
Event7th ACM Conference on Recommender Systems, RecSys 2013 - Hong Kong, China
Duration: Oct 12 2013Oct 16 2013

Publication series

NameRecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems


Other7th ACM Conference on Recommender Systems, RecSys 2013
CityHong Kong


  • Collaborative filtering
  • Learning to rank
  • Matrix factorization

ASJC Scopus subject areas

  • Software


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