2 citations · 3 across the 4 of their papers we have counts for
4 papers
MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
Xin Song, Zhilin Guan, Ruidong Han +12
Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and str…
Multi-Interest Recommendation: A Survey
Zihao Li, Qiang Chen, Lixin Zou +2
Existing recommendation methods often struggle to model users' multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty an…
Efficient Sparse Attention needs Adaptive Token Release
Chaoran Zhang, Lixin Zou, Dan Luo +4
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their `large' scale introduces significa…
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
Zuoli Tang, Zhaoxin Huan, Zihao Li +6
Sequential recommendation systems aim to predict users' next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing…