# LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation

arXiv: https://arxiv.org/abs/2508.15486 · HTML: https://mlsanity.com/paper/2508.15486/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale](https://mlsanity.com/paper/2604.24806/) (2026)
- [Towards a Densing Law for User Representation Learning at Billion-Scale Capacity](https://mlsanity.com/paper/2608.23392/) (2026)

## Abstract (arXiv)

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.