Papers › Collaborative Metric Learning
Collaborative Metric Learning
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | Million Song Dataset | CML | Recall@100 | 0.3022 | #7 of 7 | Archive leaderboard | report |
| Recommendation Systems | Million Song Dataset | CML | Recall@50 | 0.2460 | #7 of 7 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | CML | HR@10 | 0.7216 | #21 of 31 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | CML | nDCG@10 | 0.5413 | #21 of 31 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | CML | HR@10 | 0.7764 | #3 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | CML | Recall@100 | 0.6022 | #3 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | CML | Recall@50 | 0.4665 | #3 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | CML | nDCG@10 | 0.5301 | #3 of 18 | Archive leaderboard | report |
| Recommendation Systems | Netflix | CML | Recall@10 | 0.4612 | #9 of 10 | Archive leaderboard | report |
| Recommendation Systems | Netflix | CML | nDCG@10 | 0.2948 | #9 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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