5 papers
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…
Learning to Reflect and Correct: Towards Better Decoding Trajectories for Large-Scale Generative Recommendation
Haibo Xing, Hao Deng, Lingyu Mu +4
Generative Recommendation (GR) has become a promising paradigm for large-scale recommendation systems. However, existing GR models typically perform single-pass decoding without ex…
Masked Diffusion Generative Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +4
Generative recommendation (GR) typically first quantizes continuous item embeddings into multi-level semantic IDs (SIDs), and then generates the next item via autoregressive decodi…
Synergistic Integration and Discrepancy Resolution of Contextualized Knowledge for Personalized Recommendation
Lingyu Mu, Hao Deng, Haibo Xing +7
The integration of large language models (LLMs) into recommendation systems has revealed promising potential through their capacity to extract world knowledge for enhanced reasonin…