1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.IR2026
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…
cs.IR2025
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…
cs.CL2024★ 1 cited
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…