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Models/Wide & Deep

Wide & Deep

Reported on 6 benchmarks across 1 task · 1 paper · 6 SOTA

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

Miscellaneous6 results

  • Click-Through Rate PredictiononBing News
    AUC· 2016-06-24
    0.8377
    best: 0.84 (xDeepFM)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792
  • Click-Through Rate PredictiononBing News
    Log Loss· 2016-06-24
    0.2668
    best: 0.2649 (xDeepFM)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792
  • Click-Through Rate PredictiononDianping
    AUC· 2016-06-24
    0.8361
    best: 0.8639 (xDeepFM)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792
  • Click-Through Rate PredictiononDianping
    Log Loss· 2016-06-24
    0.3364
    best: 0.3156 (xDeepFM)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792
  • Click-Through Rate PredictiononMovieLens 20M
    AUC· 2016-06-24
    0.7304
    best: 0.79 (github.com/guotong1988/movielens_dataset)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792
  • Click-Through Rate PredictiononAmazon
    AUC· 2016-06-24
    0.8637
    best: 0.8871 (DIN + Dice Activation)
    SOTA
    Wide & Deep Learning for Recommender SystemsarXiv:1606.07792