4 papers
GEMs: Breaking the Long-Sequence Barrier in Generative Recommendation with a Multi-Stream Decoder
Yu Zhou, Chengcheng Guo, Kuo Cai +6
While generative recommendations (GR) possess strong sequential reasoning capabilities, they face significant challenges when processing extremely long user behavior sequences: the…
OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation
Shen Wang, Yusheng Huang, Ruochen Yang +15
Live-streaming recommender system serves as critical infrastructure that bridges the patterns of real-time interactions between users and authors. Similar to traditional industrial…
PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
Chengcheng Guo, Kuo Cai, Yu Zhou +5
Generative Recommendation has emerged as a promising paradigm, reformulating recommendation as a sequence-to-sequence generation task over hierarchical Semantic IDs. However, exist…
MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation
Chengcheng Guo, Junda She, Kuo Cai +5
Large-scale industrial recommendation systems typically employ a two-stage paradigm of retrieval and ranking to handle huge amounts of information. Recent research focuses on impro…