# FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

arXiv: https://arxiv.org/abs/2304.00902 · HTML: https://mlsanity.com/paper/2304.00902/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2023.

## Built on by (most cited first, 30 of 37)

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- [The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion](https://mlsanity.com/paper/2505.05605/) (2025)
- [Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction](https://mlsanity.com/paper/2505.17999/) (2025)
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- [Exploiting Field Dependencies for Learning on Categorical Data](https://mlsanity.com/paper/2307.09321/) (2023)
- [Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction](https://mlsanity.com/paper/2601.19142/) (2026)
- [Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction](https://mlsanity.com/paper/2308.02568/) (2023)
- [Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction](https://mlsanity.com/paper/2408.08713/) (2024)
- [Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao](https://mlsanity.com/paper/2411.13057/) (2024)
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- [Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework](https://mlsanity.com/paper/2602.12972/) (2026)
- [DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction](https://mlsanity.com/paper/2305.04891/) (2023)
- [UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style](https://mlsanity.com/paper/2402.10381/) (2024)
- [Polyhedral Conic Classifier for CTR Prediction](https://mlsanity.com/paper/2406.03892/) (2024)
- [From Features to Transformers: Redefining Ranking for Scalable Impact](https://mlsanity.com/paper/2502.03417/) (2025)
- [Addressing Information Loss and Interaction Collapse: A Dual Enhanced Attention Framework for Feature Interaction](https://mlsanity.com/paper/2503.11233/) (2025)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives](https://mlsanity.com/paper/2512.01179/) (2025)
- [LLM-Empowered Representation Learning for Emerging Item Recommendation](https://mlsanity.com/paper/2512.10370/) (2025)
- [Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection](https://mlsanity.com/paper/2601.10067/) (2026)

## Abstract (arXiv)

Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models.