Learning to Rank

By Hang Li. 2009

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Document Ranking with a Pretrained Sequence-to-Sequence Model (2020, neutral): “Prior to deep learning, researchers and practitioners mostly adopt the paradigm known as “learning to rank”, which is heavily driven by manual feature engineering (Liu, 2009; Li, 2011).”
Multi-Stage Document Ranking with BERT (2019, neutral): “Although machine-learned ranking models have been well studied since the mid-2000s under the banner of “learning to rank”, the paradigm is heavily driven by manual feature engineering (Liu, 2009; Li, 2011); commercial web search engines are known to incorporate thousands of features (or more) in the”
BEER: BEtter Evaluation as Ranking (2014, background): “The output of learning in this approach can be either a function that ranks all hypotheses directly (global ranking model) or a function that assigns a score to each hypothesis individually which can be used for ranking (local ranking model) (Li, 2011).”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, methodology): “PL can be seen as a natural link between ML and decision support, and was primarily applied in information retrieval with the central task of learning to rank [56, 57].”