49 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Meizu (China)CN3 papers
- Carnegie Mellon UniversityUS2 papers
- Hong Kong University of Science and TechnologyHK2 papers
- Zhejiang UniversityCN2 papers
- Zhejiang University of TechnologyCN2 papers
- Beijing University of Posts and TelecommunicationsCN1 paper
- Chengdu Institute of Information Technology (China)CN1 paper
- Institute of SoftwareCN1 paper
- Meituan (China)CN1 paper
- Nanjing University of Information Science and TechnologyCN1 paper
- Peking UniversityCN1 paper
- Tsinghua UniversityCN1 paper
Showing cs.IRShow all
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cs.IR2026
Sample Is Feature: Beyond Item-Level, Toward Sample-Level Tokens for Unified Large Recommender Models
Shuli Wang, Junwei Yin, Changhao Li +6
Scaling industrial recommender models has followed two parallel paradigms: \textbf{sample information scaling} -- enriching the information content of each training sample through…
cs.IR2026
Unleashing the Potential of Sparse Attention on Long-term Behaviors for CTR Prediction
Weijiang Lai, Beihong Jin, Di Zhang +5
In recent years, the success of large language models (LLMs) has driven the exploration of scaling laws in recommender systems. However, models that demonstrate scaling laws are ac…