# Field-Aware RankMixer with Dual-Stream Bilinear Fusion for the Tencent UNI-REC Challenge

arXiv: https://arxiv.org/abs/2607.15590 · HTML: https://mlsanity.com/paper/2607.15590/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Muon is Scalable for LLM Training](https://mlsanity.com/paper/2502.16982/) (2025)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [RankMixer: Scaling Up Ranking Models in Industrial Recommenders](https://mlsanity.com/paper/2507.15551/) (2025)
- [OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender](https://mlsanity.com/paper/2510.26104/) (2025)
- [UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems](https://mlsanity.com/paper/2604.00590/) (2026)
- [TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds](https://mlsanity.com/paper/2604.13737/) (2026)

## Built on by (most cited first, 1 of 1)

- [KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation](https://mlsanity.com/paper/2609.39828/) (2026)

## Abstract (arXiv)

This paper presents our solution to the KDD Cup 2026 Tencent UNIREC Challenge. The task requires joint modeling of multi-domain user behavior sequences and non-sequential multi-field features for target-ad pCVR prediction. We develop a Field-Aware RankMixer (FA-RankMixer) with dual-stream bilinear fusion. The model first applies target-aware DIN modules to extract user interests from multiple behavior domains. It also models recent and earlier interests separately for the longest behavior sequence. The model then forms semantic tokens based on feature fields and behavior domains and uses RankMixer blocks for cross-token interaction. A shallow MLP stream complements the deep RankMixer stream, and a group-wise bilinear module fuses their representations. Our final solution ranks ninth on the official leaderboard. Our code is available at https://github.com/PixelCookie-zyf/TAAC-2026-SeRankMixer.