Collaborative Metric Learning
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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Datasets
MovieLens
Netflix Prize
Results from the Paper
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(Recall@100 metric)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Recommendation Systems | Million Song Dataset | CML | Recall@50 | 0.2460 | # 7 | |
| Recall@100 | 0.3022 | # 1 | ||||
| Recommendation Systems | MovieLens 1M | CML | HR@10 | 0.7216 | # 6 | |
| nDCG@10 | 0.5413 | # 5 | ||||
| Recommendation Systems | MovieLens 20M | CML | Recall@50 | 0.4665 | # 9 | |
| HR@10 | 0.7764 | # 3 | ||||
| nDCG@10 | 0.5301 | # 3 | ||||
| Recall@100 | 0.6022 | # 1 | ||||
| Recommendation Systems | Netflix | CML | nDCG@10 | 0.2948 | # 3 | |
| Recall@10 | 0.4612 | # 2 |
