5 papers
OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
Yinqi Zhang, Peiyu Hu, Yuntian Tang +16
Platform-scale recommender systems often span multiple business streams such as organic recommendation, advertising, and merchant services, where user behaviors form a continuous c…
FedCGR: Federated Cross-Domain Generative Recommendation
Zhuodong Liu, Hugen Lv, Xiangyu Li +2
Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anch…
Hierarchical Latent Reasoning for LLM-based Recommendation
Peiyu Hu, Siying Gu, Weihai Lu +8
Large Language Models (LLMs) have shown strong potential for recommendation by leveraging their semantic understanding and contextual modeling capabilities. Recent studies further…
From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization
Peiyu Hu, Wayne Lu, Jia Wang
Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/ite…
Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning
Haichao Zhang, Chong Zhang, Peiyu Hu +2
Modern recommender systems face a critical challenge in complying with privacy regulations like the 'right to be forgotten': removing a user's data without disrupting recommendatio…