TY - GEN

T1 - Collaborative metric learning

AU - Hsieh, Cheng Kang

AU - Yang, Longqi

AU - Cui, Yin

AU - Lin, Tsung Yi

AU - Belongie, Serge

AU - Estrin, Deborah

N1 - Funding Information: We appreciate the anonymous reviewers for their helpful comments and feedback. This research is partly funded by AOL-Program for Connected Experiences, a Google Focused Research Award and further supported by the small data lab at Cornell Tech which receives funding from UnitedHealth Group, Google, Pfizer, RWJF, NIH and NSF. Publisher Copyright: © 2017 International World Wide Web Conference Committee (IW3C2).

PY - 2017

Y1 - 2017

N2 - Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users’ preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users’ fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.

AB - Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users’ preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users’ fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.

UR - http://www.scopus.com/inward/record.url?scp=85041898938&partnerID=8YFLogxK

U2 - 10.1145/3038912.3052639

DO - 10.1145/3038912.3052639

M3 - Conference article

AN - SCOPUS:85041898938

SP - 193

EP - 201

JO - 26th International World Wide Web Conference, WWW 2017

JF - 26th International World Wide Web Conference, WWW 2017

T2 - 26th International World Wide Web Conference, WWW 2017

Y2 - 3 April 2017 through 7 April 2017

ER -