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New research tackles ultra-long sequence modeling for recommendation systems

By PulseAugur Editorial · [3 sources] ·

Two new research papers address the challenge of modeling ultra-long user behavior sequences in recommendation systems. The first paper, SequenceO1, introduces an end-to-end framework deployed at Douyin that uses Sketch Attention and Stacked Target-to-History Cross Attention to compress and reason over histories up to 100,000 interactions. The second paper proposes a two-stage framework that closes the long-short view performance gap without relying on cached histories, by modifying scoring mechanisms and fine-tuning specific components. AI

IMPACT These methods aim to improve the efficiency and effectiveness of recommendation systems by enabling them to process and learn from much longer user interaction histories.

RANK_REASON Two academic papers published on arXiv presenting novel methods for sequence modeling in recommendation systems.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research tackles ultra-long sequence modeling for recommendation systems

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Lin Guan, Jia-Qi Yang, Zhishan Zhao, Jiaqi Huang, Hangyu Wang, Longbin Li, Beichuan Zhang, Haonan Jiang, Jinan Ni, Xiangyu Fan, Xiaowen Li, Ziyao Ren, Yuhang Qi, Xiaolong Zhu, Xuanyuan Luo, Qiwei Chen, Yi Cheng, Lele Yu ·

    SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching

    arXiv:2609.08443v1 Announce Type: cross Abstract: Modeling long-term user behavior is central to sequential recommendation and billion-scale industrial recommender systems, yet production ranking models operate under strict latency, memory, communication, and training-throughput …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Lele Yu ·

    SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching

    Modeling long-term user behavior is central to sequential recommendation and billion-scale industrial recommender systems, yet production ranking models operate under strict latency, memory, communication, and training-throughput constraints. At the 100K scale, the challenge exte…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · James Caverlee ·

    Closing the Long-Short View Gap in Sequential Recommendation without Cached History

    Sequential recommenders are typically trained on long user histories to capture rich behavioral signals, yet serving with training-length sequences is often impractical due to real-time efficiency constraints. Directly using only recent behaviors leads to a severe performance dro…

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