4 citations · 6 across the 7 of their papers we have counts for
9 papers
RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
Xinwei Yang, Kelong Mao, Yudong Guo +4
Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users…
Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning
Lirui Luo, Kelong Mao, Heming Xia +10
Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use reinforcement learning to m…
A Better Spur Should Start From Each Objective
Shanwen Mao, Hao Zhang, Guangtao nie +4
Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalariza…
Learning from Online User Feedback for Shopping Agents
Haobo Zhang, Kelong Mao, Sulong Xu +2
Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable s…
ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons
Adrian Li, Kelong Mao, Yudong Guo +7
Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product. Such combo-shopping tasks arise in device setup, meal prepa…
DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging
Binglei Zhao, Xuanhua Yang, Xiwei Zhao +1
In industrial recommendation systems, the re-ranking stage balances business objectives and diversity for sequence-level optimization while modeling contextual information. However…
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use
Jiayin Zhu, Kelong Mao, Yudong Guo +4
Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, ove…
A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Binglei Zhao, Houying Qi, Guang Xu +5
Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ra…
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
Yuanbo Gao, Peng Lin, Dongyue Wang +4
Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that h…