From the 1 of 7 linked papers with an AI index.
7 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…
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…
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Kun Yuan, Junyu Bi, Daixuan Cheng +5
Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory…
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…