Product-based Neural Networks for User Response Prediction

1 Nov 2016Yanru QuHan CaiKan RenWeinan ZhangYong YuYing WenJun Wang

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Click-Through Rate Prediction Amazon PNN AUC 0.8679 # 4
Click-Through Rate Prediction Bing News PNN AUC 0.8321 # 4
Click-Through Rate Prediction Bing News PNN Log Loss 0.2775 # 4
Click-Through Rate Prediction Company* PNN* AUC 0.8672 # 4
Click-Through Rate Prediction Company* PNN* Log Loss 0.02636 # 4
Click-Through Rate Prediction Company* IPNN AUC 0.8664 # 5
Click-Through Rate Prediction Company* IPNN Log Loss 0.02637 # 5
Click-Through Rate Prediction Company* OPNN AUC 0.8658 # 7
Click-Through Rate Prediction Company* OPNN Log Loss 0.02641 # 7
Click-Through Rate Prediction Criteo IPNN AUC 0.7972 # 6
Click-Through Rate Prediction Criteo IPNN Log Loss 0.45323 # 6
Click-Through Rate Prediction Criteo OPNN AUC 0.7982 # 4
Click-Through Rate Prediction Criteo OPNN Log Loss 0.45256 # 5
Click-Through Rate Prediction Criteo PNN* AUC 0.7987 # 3
Click-Through Rate Prediction Criteo PNN* Log Loss 0.45214 # 4
Click-Through Rate Prediction Dianping PNN AUC 0.8445 # 3
Click-Through Rate Prediction Dianping PNN Log Loss 0.3424 # 4
Click-Through Rate Prediction iPinYou PNN* AUC 0.7661 # 3
Click-Through Rate Prediction iPinYou OPNN AUC 0.8174 # 1
Click-Through Rate Prediction iPinYou IPNN AUC 0.7914 # 2
Click-Through Rate Prediction MovieLens 20M PNN AUC 0.7321 # 4