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

TL;DR
OneMall is a unified, end-to-end generative recommendation framework for e-commerce at Kuaishou, integrating multiple scenarios with a Transformer-based architecture and reinforcement learning, leading to significant business metric improvements.
Contribution
It introduces a comprehensive architecture unifying diverse e-commerce scenarios with novel components like semantic tokenization and RL-based optimization, tailored for large language model integration.
Findings
Achieved over 13 ext% GMV increase in product-card scenario
Generated 15.32 ext% more orders in Short-Video scenario
Deployed at Kuaishou serving 400 million users
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Multimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI)
