Learning to Rank
Pointwise, pairwise and listwise objectives, LambdaMART, position bias and counterfactual training.
7concepts
133flashcards
52minutes of reading
- 01 Click Models: Cascade, DBN and UBM Generative models of how users scan and click a result page, which let a search log be turned into relevance estimates by modelling what each user probably examined and whether a click satisfied them.
- 02 Counterfactual Learning to Rank Training a ranker on logged clicks while correcting for the bias in how those clicks were generated, which turns a biased log into an unbiased estimate of a ranking objective.
- 03 LambdaRank and LambdaMART The trick of defining a gradient without ever defining a loss, which lets gradient boosting optimise a discontinuous ranking metric directly, and why the result dominated learning to rank for a decade.
- 04 Position Bias and the Examination Hypothesis Clicks measure relevance multiplied by the chance the user looked, so training on raw clicks teaches a ranker to reproduce whatever ranking generated the logs.