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

• URL: http://arxiv.org/abs/2601.21770v2
• Date: Mon, 02 Feb 2026 15:05:42 GMT
• Title: OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce
• Authors: Kun Zhang, Jingming Zhang, Wei Cheng, Yansong Cheng, Jiaqi Zhang, Hao Lu, Xu Zhang, Haixiang Gan, Jiangxia Cao, Tenglong Wang, Ximing Zhang, Boyang Xia, Kuo Cai, Shiyao Wang, Hongjian Dou, Jinkai Yu, Mingxing Wen, Qiang Luo, Dongxu Liang, Chenyi Lei, Jun Wang, Runan Liu, Zhaojie Liu, Ruiming Tang, Tingting Gao, Shaoguo Liu, Yuqing Ding, Hui Kong, Han Li, Guorui Zhou, Wenwu Ou, Kun Gai,
• Abstract summary: OneMall is an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou.<n>It unifies the e-commerce's multiple item distribution scenarios, such as Product-card, short-video and live-streaming.<n>OneMall has been deployed, serving over 400 million daily active users at Kuaishou.
• Score: 68.7552227901176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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