Papers › Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, Yuzhou Zhang
Easy-to-use,Modular and Extendible package of deep-learning based CTR models.DeepFM,DeepInterestNetwork(DIN),DeepInterestEvolutionNetwork(DIEN),DeepCrossNetwork(DCN),AttentionalFactorizationMachine(AFM),Neural Factorization Machine(NFM),AutoInt,Deep Session Interest Network(DSIN)
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Click-Through Rate Prediction | Avazu | FGCNN+IPNN | AUC | 0.7883 | #9 of 15 | Archive leaderboard | report |
| Click-Through Rate Prediction | Avazu | FGCNN+IPNN | LogLoss | 0.3746 | #9 of 15 | Archive leaderboard | report |
| Click-Through Rate Prediction | Huawei App Store | FGCNN+IPNN | AUC | 0.9407 | #1 of 1 | Archive leaderboard | report |
| Click-Through Rate Prediction | Huawei App Store | FGCNN+IPNN | Log Loss | 0.1134 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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