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Models/CML

CML

Reported on 10 benchmarks across 1 task

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Knowledge Base10 results

  • Recommendation SystemsonMovieLens 20M
    HR@10
    0.7764
    best: 0.8736 (HyperML)
  • Recommendation SystemsonMovieLens 20M
    Recall@100
    0.6022
  • Recommendation SystemsonMovieLens 20M
    Recall@50
    0.4665
    best: 0.553 (RecVAE)
  • Recommendation SystemsonMovieLens 20M
    nDCG@10
    0.5301
    best: 0.6404 (HyperML)
  • Recommendation SystemsonMillion Song Dataset
    Recall@100
    0.3022
  • Recommendation SystemsonMillion Song Dataset
    Recall@50
    0.246
    best: 0.428 (EASE)
  • Recommendation SystemsonNetflix
    Recall@10
    0.4612
    best: 0.5371 (LRML)
  • Recommendation SystemsonNetflix
    nDCG@10
    0.2948
    best: 0.3578 (LRML)
  • Recommendation SystemsonMovieLens 1M
    HR@10
    0.7216
    best: 0.8903 (KTUP (soft))
  • Recommendation SystemsonMovieLens 1M
    nDCG@10
    0.5413
    best: 0.6292 (SSE-PT)