Collaborative metric learning
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | 26th International World Wide Web Conference, WWW 2017 |
Pages (from-to) | 193-201 |
Number of pages | 9 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | 26th International World Wide Web Conference, WWW 2017 - Perth, Australia Duration: 3 Apr 2017 → 7 Apr 2017 |
Conference
Conference | 26th International World Wide Web Conference, WWW 2017 |
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Country | Australia |
City | Perth |
Period | 03/04/2017 → 07/04/2017 |
Sponsor | Bankwest, Curtin University, Edith Cowan University (ECU), et al., Murdoch University, University of Western Australia |
Bibliographical note
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).
ID: 301827073