From the 2 of 17 linked papers with an AI index.
17 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…
Learning from the Future: Privileged Self-Distillation for Sequential Recommendation
Jiakai Tang, Yang Zhang, See-Kiong Ng +4
The paper introduces Privileged Self-Distillation (PSD), a method that uses future user interactions as training‑only privileged information to improve sequential recommendation mo…
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…
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…
Generative Pseudo-Labeling for Pre-Ranking with LLMs
Junyu Bi, Xinting Niu, Daixuan Cheng +4
Pre-ranking is a critical stage in industrial recommendation systems, tasked with efficiently scoring thousands of recalled items for downstream ranking. A key challenge is the tra…