TY - GEN
T1 - Retargeted matrix factorization for collaborative filtering
AU - Koyejo, Oluwasanmi
AU - Acharyya, Sreangsu
AU - Ghosh, Joydeep
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Learning to rank
KW - Matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=84887598073&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887598073&partnerID=8YFLogxK
U2 - 10.1145/2507157.2507185
DO - 10.1145/2507157.2507185
M3 - Conference contribution
AN - SCOPUS:84887598073
SN - 9781450324090
T3 - RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
SP - 49
EP - 56
BT - RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
T2 - 7th ACM Conference on Recommender Systems, RecSys 2013
Y2 - 12 October 2013 through 16 October 2013
ER -