2 citations · 2 across the 5 of their papers we have counts for
5 papers
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Renzhi Wu, Zikun Cui, Junjie Yang +10
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet exi…
CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Zikun Cui, Renzhi Wu, Junjie Yang +10
Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuanc…
RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
Renzhi Wu, Junjie Yang, Li Chen +3
Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains. To address this, we introduce RankGra…
Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention
Rengan Xu, Junjie Yang, Yifan Xu +17
The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previou…
Async Learned User Embeddings for Ads Delivery Optimization
Mingwei Tang, Meng Liu, Hong Li +16
In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maint…