6 papers
RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems
Weidi Pan, He Ma, Shuhao Ye +6
The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recommender models. This paper prese…
TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
He Ma, Chen Liu
Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively. However, integrat…
MetaGen: Self-Evolving Roles and Topologies for Multi-Agent LLM Reasoning
Yimeng Wang, Jiaxing Zhao, Hongbin Xie +6
Large language models are increasingly deployed as multi-agent systems, where specialized roles communicate and collaborate through structured interactions to solve complex tasks t…
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
Jianling Wang, Yifan Liu, Yinghao Sun +11
Exploration, the act of broadening user experiences beyond their established preferences, is challenging in large-scale recommendation systems due to feedback loops and limited sig…
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Jianling Wang, Haokai Lu, Yifan Liu +9
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel u…