Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the ranking stage, whereas research for the candidate retrieval stage remains under-explored. This paper presents LongRetriever, a practical framework for incorporating ultra-long sequences into the retrieval stage of recommenders. Specifically, we propose in-context training and multi-context retrieval, which enable candidate-specific interaction between user sequence and candidate item, and ensure training-serving consistency under the search-based paradigm. Extensive online A/B testing conducted on a large-scale e-commerce platform demonstrates statistically significant improvements, confirming the framework's effectiveness. Currently, LongRetriever has been fully deployed in the platform, impacting billions of users.
@article{arxiv.2508.15486,
title = {LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation},
author = {Qin Ren and Zheng Chai and Xijun Xiao and Yuchao Zheng and Di Wu},
journal= {arXiv preprint arXiv:2508.15486},
year = {2025}
}