14 papers
EvoRec: Self Evolving Agentic Recommender Systems
Lingyu Mu, Hao Deng, Haibo Xing +3
Optimizing modern recommender systems still relies heavily on engineers iterating by hand, which is slow and bounded by individual expertise. LLM-based agents open a path toward au…
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Peisong Zhang, Manqiang Peng, Yuxuan Wu +21
Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing co…
LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +7
Recent progress in large language model (LLM) based generative recommendation (GR) shows that leveraging LLM world knowledge can substantially improve performance. However, existin…
Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Jinxin Hu, Hao Deng, Lingyu Mu +4
Large-scale industrial recommenders typically use a fixed multi-stage pipeline (recall, ranking, re-ranking) and have progressed from collaborative filtering to deep and large pre-…
AgenticRS-Architecture: System Design for Agentic Recommender Systems
Hao Zhang, Jinxin Hu, Hao Deng +4
AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendatio…
RCLRec: Reverse Curriculum Learning for Modeling Sparse Conversions in Generative Recommendation
Yulei Huang, Hao Deng, Haibo Xing +5
Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organiz…