Collaborative metric learning
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Collaborative metric learning. / Hsieh, Cheng Kang; Yang, Longqi; Cui, Yin; Lin, Tsung Yi; Belongie, Serge; Estrin, Deborah.
In: 26th International World Wide Web Conference, WWW 2017, 2017, p. 193-201.Research output: Contribution to journal › Conference article › Research › peer-review
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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 -
ID: 301827073