Papers › STEC: See-Through Transformer-based Encoder for CTR Prediction
STEC: See-Through Transformer-based Encoder for CTR Prediction
Serdarcan Dilbaz, Hasan Saribas
Click-Through Rate (CTR) prediction holds a pivotal place in online advertising and recommender systems since CTR prediction performance directly influences the overall satisfaction of the users and the revenue generated by companies. Even so, CTR prediction is still an active area of research since it involves accurately modelling the preferences of users based on sparse and high-dimensional features where the higher-order interactions of multiple features can lead to different outcomes. Most CTR prediction models have relied on a single fusion and interaction learning strategy. The few CTR prediction models that have utilized multiple interaction modelling strategies have treated each interaction to be self-contained. In this paper, we propose a novel model named STEC that reaps the benefits of multiple interaction learning approaches in a single unified architecture. Additionally, our model introduces residual connections from different orders of interactions which boosts the performance by allowing lower level interactions to directly affect the predictions. Through extensive experiments on four real-world datasets, we demonstrate that STEC outperforms existing state-of-the-art approaches for CTR prediction thanks to its greater expressive capabilities.
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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 | Criteo | STEC | AUC | 0.8143 | #9 of 39 | Archive leaderboard | report |
| Click-Through Rate Prediction | Criteo | STEC | Log Loss | 0.4379 | #9 of 39 | Archive leaderboard | report |
| Click-Through Rate Prediction | MovieLens 1M | STEC | AUC | 0.9712 | #1 of 6 | Archive leaderboard | report |
| Click-Through Rate Prediction | MovieLens 1M | STEC | Log Loss | 0.2016 | #1 of 6 | 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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