Collaborative metric learning
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Tidsskrift | 26th International World Wide Web Conference, WWW 2017 |
Sider (fra-til) | 193-201 |
Antal sider | 9 |
DOI | |
Status | Udgivet - 2017 |
Eksternt udgivet | Ja |
Begivenhed | 26th International World Wide Web Conference, WWW 2017 - Perth, Australien Varighed: 3 apr. 2017 → 7 apr. 2017 |
Konference
Konference | 26th International World Wide Web Conference, WWW 2017 |
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Land | Australien |
By | Perth |
Periode | 03/04/2017 → 07/04/2017 |
Sponsor | Bankwest, Curtin University, Edith Cowan University (ECU), et al., Murdoch University, University of Western Australia |
Bibliografisk note
Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2).
ID: 301827073