6 citations · 30 across the 34 of their papers we have counts for
7 papers · 1 filter
Towards Faithful Simulation of Human Shopping Behavior
Jiakai Tang, Yan Mi, Jing Yu +9
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made en…
Learning from the Future: Privileged Self-Distillation for Sequential Recommendation
Jiakai Tang, Yang Zhang, See-Kiong Ng +4
Sequential recommenders are commonly trained with one-hot next-item labels under a causal (prefix-only) objective aligned with inference. While deployment-compatible, this supervis…
Self-Evolving World Models for LLM Agent Planning
Xuan Zhang, Wenxuan Zhang, See-Kiong Ng +1
World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignor…
Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation
Miaomiao Cai, Yunshan Ma, Fangqi Zhu +5
Multi-behavior recommendation improves target-behavior prediction by exploiting heterogeneous auxiliary feedback (e.g., view, collect, and cart), yet its robustness is undermined b…
FineVerify: Scaling Test-Time Compute with Fine-Grained Self-Verification for Agentic Search
James Xu Zhao, Hui Chen, Bryan Hooi +1
Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute is a promising way to improve thes…
Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation
Shijie Wang, Chengyi Liu, Yujuan Ding +4
Large language models (LLMs) have recently been adopted for recommendations due to their ability to understand user intent and item semantics. However, LLM-based recommender system…