24 citations · 30 across the 3 of their papers we have counts for
3 papers
cs.AI2024
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
Tingjia Shen, Hao Wang, Chuhan Wu +7
Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational…
cs.IR2024★ 24 cited
Embedding Compression in Recommender Systems: A Survey
Shiwei Li, Huifeng Guo, Xing Tang +4
To alleviate the problem of information explosion, recommender systems are widely deployed to provide personalized information filtering services. Usually, embedding tables are emp…
cs.LG2024★ 6 cited
Entropy Law: The Story Behind Data Compression and LLM Performance
Mingjia Yin, Chuhan Wu, Yufei Wang +7
Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much…