Collaborative Metric Learning

2017-04-01 · WWW 2017 4 · Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, Deborah Estrin

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.

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Code (2)

changun/CollMetric 공식 구현 tf

statusrank/LibCML pytorch

Tasks

Collaborative Filtering Metric Learning Recommendation Systems

Methods 이 논문이 사용한 방법론

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