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. read more

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Results from the Paper

 Ranked #1 on Recommendation Systems on MovieLens 20M (Recall@100 metric)

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Recommendation Systems Million Song Dataset CML Recall@50 0.2460 # 6
Recall@100 0.3022 # 1
Recommendation Systems MovieLens 1M CML HR@10 0.7216 # 6
nDCG@10 0.5413 # 4
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 HR@10 0.4612 # 2
nDCG@10 0.2948 # 2


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