# OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce

arXiv: https://arxiv.org/abs/2601.21770 · HTML: https://mlsanity.com/paper/2601.21770/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [OneReason Technical Report](https://mlsanity.com/paper/2606.06260/) (2026)
- [KUAISHOU Explorer LLM-Rec Challenge 2026: Reasoning Generative Recommendation](https://mlsanity.com/paper/2609.39828/) (2026)
- [WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat](https://mlsanity.com/paper/2610.16979/) (2026)

## Abstract (arXiv)

In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematically unifies the e-commerce's multiple item distribution scenarios, such as Product-card, short-video and live-streaming. Specifically, it comprises three key components, aligning the entire model training pipeline to the LLM's pre-training/post-training: (1) E-commerce Semantic Tokenizer: we provide a tokenizer solution that captures both real-world semantics and business-specific item relations across different scenarios; (2) Transformer-based Architecture: we largely utilize Transformer as our model backbone, e.g., employing Query-Former for long sequence compression, Cross-Attention for multi-behavior sequence fusion, and Sparse MoE for scalable auto-regressive generation; (3) Reinforcement Learning Pipeline: we further connect retrieval and ranking models via RL, enabling the ranking model to serve as a reward signal for end-to-end policy retrieval model optimization. Extensive experiments demonstrate that OneMall achieves consistent improvements across all e-commerce scenarios: +13.01% GMV in product-card, +15.32% Orders in Short-Video, and +2.78% Orders in Live-Streaming. OneMall has been deployed, serving over 400 million daily active users at Kuaishou.