Sequence Modeling Papers

Summary Statistics

  • Total Papers: 11
  • Companies: 5

ByteDance (4 papers)

# Paper Title Authors Year Focus
1 OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender Zhaoqi Zhang, Haolei Pei, Jun Guo, et al. 2026 Recommendation Systems, Sequence Modeling
2 HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction Yunwen Huang, Shiyong Hong, Xijun Xiao, et al. 2026 CTR Prediction, Sequence Modeling
3 TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders Yuchen Jiang, Jie Zhu, Xintian Han, et al. 2026 Ranking Models, Scaling Laws
4 RankMixer: Scaling Up Ranking Models in Industrial Recommenders Jie Zhu, Zhifang Fan, Xiaoxie Zhu, et al. 2025 Ranking Models, Scaling Laws

Alibaba (2 papers)

# Paper Title Authors Year Focus
1 HHFT: Hierarchical Heterogeneous Feature Transformer for Recommendation Systems Liren Yu, Wenming Zhang, Silu Zhou, et al. 2025 Recommendation Systems, Feature Interaction
2 FAT: From Scaling to Structured Expressivity - Rethinking Transformers for CTR Prediction Bencheng Yan, Yuejie Lei, Zhiyuan Zeng, et al. 2025 CTR Prediction, Transformers

Meta (2 papers)

# Paper Title Authors Year Focus
1 LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation Lee Xiong, Zhirong Chen, Rahul Mayuranath, et al. 2026 Ads Recommendation, Scaling Laws
2 InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction Zhichen Zeng, Xiaolong Liu, Mengyue Hang, et al. 2025 CTR Prediction, Heterogeneous Interaction

Google (1 paper)

# Paper Title Authors Year Focus
1 HiFormer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems Huan Gui, Ruoxi Wang, Ke Yin, et al. 2023 Recommendation Systems, Feature Interaction

Kuaishou (1 paper)

# Paper Title Authors Year Focus
1 UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems Mingming Ha, Guanchen Wang, Linxun Chen, et al. 2026 Recommendation Systems, Scaling Laws

Key Research Topics

By Topic Frequency

  • Scaling Laws: 5 papers (RankMixer, TokenMixer-Large, UniMixer, LLaTTE, FAT)
  • Recommendation Systems: 5 papers (HHFT, OneTrans, HyFormer, UniMixer, HiFormer)
  • CTR Prediction: 4 papers (HyFormer, FAT, InterFormer, HHFT)
  • Feature Interaction: 4 papers (HHFT, OneTrans, HiFormer, InterFormer)
  • Sequence Modeling: 3 papers (OneTrans, HyFormer, LLaTTE)
  • Ranking Models: 2 papers (RankMixer, TokenMixer-Large)

By Year

  • 2026: 5 papers (OneTrans, HyFormer, TokenMixer-Large, LLaTTE, UniMixer)
  • 2025: 4 papers (RankMixer, HHFT, FAT, InterFormer)
  • 2023: 1 paper (HiFormer)

Notes

  • ByteDance leads with 4 papers, focusing on ranking models and sequence modeling
  • Alibaba and Meta each contribute 2 papers on CTR prediction and recommendation systems
  • Google and Kuaishou each have 1 paper on recommendation systems
  • Scaling laws is the dominant research direction across companies
  • Most papers are from 2025-2026, indicating recent focus on industrial applications