Papers › Representation Learning-Assisted Click-Through Rate Prediction
Representation Learning-Assisted Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, Yanlong Du
Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically reliable feature representations, and in consequence to improve the performance of CTR prediction. In particular, DeepMCP contains three parts: a matching subnet, a correlation subnet and a prediction subnet. These subnets model the user-ad, ad-ad and feature-CTR relationship respectively. When these subnets are jointly optimized under the supervision of the target labels, the learned feature representations have both good prediction powers and good representation abilities. Experiments on two large-scale datasets demonstrate that DeepMCP outperforms several state-of-the-art models for CTR prediction.
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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 | Avito | DeepMCP | AUC | 0.7927 | #2 of 2 | Archive leaderboard | report |
| Click-Through Rate Prediction | Avito | DeepMCP | Log Loss | 0.05518 | #2 of 2 | Archive leaderboard | report |
| Click-Through Rate Prediction | Company* | DeepMCP | AUC | 0.7674 | #8 of 8 | Archive leaderboard | report |
| Click-Through Rate Prediction | Company* | DeepMCP | Log Loss | 0.2341 | #8 of 8 | 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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