11 papers
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
LoopMemGR: From Behavior Logs to Evolving Memory for Generative Recommendation
Hui Qian, Changfa Wu, Chang Liu +5
The paper introduces LoopMemGR, a framework that adds a closed-loop experience memory to generative recommendation systems, allowing them to reuse past recommendation–feedback traj…
SPARC: Sequence-aware Progressive Attribute Routing and Compression Framework for Generative Recommendation
Chang Liu, Changfa Wu, Hui Qian +5
Generative recommendation tokenizes items as discrete Semantic IDs (SIDs) and autoregressively generates target items from users' historical SID sequences. Although existing SIDs i…
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jie Peng, Yanping Zheng, Zhewei Zhe +3
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world re…
TSGR: Taobao Search Generative Retrieval
Tianyu Zhan, Gui Ling, Tong Xiong +9
Generative retrieval (GR) has demonstrated strong promise for industrial e-commerce search by training a single autoregressive model to directly generate the Semantic IDs (SIDs) of…
RecGPT-V3 Technical Report
Bowen Zheng, Chao Yi, Dian Chen +26
Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…