Pointwise Learning-to-Rank Algorithm

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A Pointwise Learning-to-Rank Algorithm is an supervised ranking algorithm that directly predicts the ordinal value for an item.



References

2017

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/learning_to_rank#Pointwise_approach Retrieved:2017-9-13.
    • In this case it is assumed that each query-document pair in the training data has a numerical or ordinal score. Then learning-to-rank problem can be approximated by a regression problem — given a single query-document pair, predict its score.

      A number of existing supervised machine learning algorithms can be readily used for this purpose. Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict score of a single query-document pair, and it takes a small, finite number of values.

2017b

Year Name Type Notes
1989 OPRF pointwise Polynomial regression (instead of machine learning, this work refers to pattern recognition, but the idea is the same)
1992 SLR pointwise Staged logistic regression
2002 Pranking pointwise Ordinal regression.
2007 McRank pointwise
2010 CRR pointwise & pairwise Combined Regression and Ranking. Uses stochastic gradient descent to optimize a linear combination of a pointwise quadratic loss and a pairwise hinge loss from Ranking SVM.