# IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System

arXiv: https://arxiv.org/abs/2210.09890 · HTML: https://mlsanity.com/paper/2210.09890/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2022.

## Built on by (most cited first, 18 of 18)

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- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
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- [A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems](https://mlsanity.com/paper/2502.10284/) (2025)
- [Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching](https://mlsanity.com/paper/2502.20687/) (2025)
- [TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter](https://mlsanity.com/paper/2302.13915/) (2023)
- [InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features](https://mlsanity.com/paper/2504.06609/) (2025)
- [Equip Pre-ranking with Target Attention by Residual Quantization](https://mlsanity.com/paper/2509.16931/) (2025)
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- [Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest](https://mlsanity.com/paper/2512.17277/) (2025)
- [Retrieval-Oriented Knowledge for Click-Through Rate Prediction](https://mlsanity.com/paper/2404.18304/) (2024)
- [Mamba for Scalable and Efficient Personalized Recommendations](https://mlsanity.com/paper/2409.17165/) (2024)
- [Target-Aware Early Stage Ranking](https://mlsanity.com/paper/2511.21095/) (2025)
- [RADAR: Recall Augmentation through Deferred Asynchronous Retrieval](https://mlsanity.com/paper/2506.07261/) (2025)
- [LIME: Link-based user-item Interaction Modeling with decoupled xor attention for Efficient test time scaling](https://mlsanity.com/paper/2510.18239/) (2025)
- [Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems](https://mlsanity.com/paper/2603.03770/) (2026)
- [Memory Layer: Train the In-Model Cache for Recommendation Models](https://mlsanity.com/paper/2607.25110/) (2026)

## Abstract (arXiv)

Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the two-tower model since the ``user-item decoupling architecture'' paradigm is able to balance the textitefficiency and textiteffectiveness. However, the cost of high efficiency is the neglect of the potential information interaction between user and item towers, hindering the prediction accuracy critically. In this paper, we show it is possible to design a two-tower model that emphasizes both information interactions and inference efficiency. The proposed model, IntTower (short for textitInteraction enhanced Two-Tower), consists of Light-SE, FE-Block and CIR modules. Specifically, lightweight Light-SE module is used to identify the importance of different features and obtain refined feature representations in each tower. FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly. Experimental results on three public datasets show that IntTower outperforms the SOTA pre-ranking models significantly and even achieves comparable performance in comparison with the ranking models. Moreover, we further verify the effectiveness of IntTower on a large-scale advertisement pre-ranking system. The code of IntTower is publicly availablefootnotehttps://github.com/archersama/IntTower