3 papers
cs.IR2026
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
Kairui Fu, Changfa Wu, Kun Yuan +8
Generative retrieval (GR) has emerged as a promising paradigm in recommendation systems by autoregressively decoding identifiers of target items. Despite its potential, current app…
cs.IR2025
RecGPT-V2 Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +32
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. Whil…
cs.IR2025
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang, Changfa Wu, Dunxian Huang +9
Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative model…