Learning To Rank
4concepts
28flashcards
28minutes of reading
- 01 Pointwise, Pairwise and Listwise Objectives Ranking is not regression, and the three families of learning-to-rank losses differ in how much of the ranking structure they put inside the objective rather than leaving to a sort.
- 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.