Learning to rank using gradient descent Learning to rank using gradient descent
Paper summary [Learning to rank using gradient descent](https://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf) is a paper published in 2005 by Burges et all from Microsoft. The paper introduced RankNet. RankNet is a neural network for recommendations. The main use-case of the paper is ranking search results. ## Key Ideas * Preprocessing: Filter results which are relevant * Ranking: Rank results which are relevant by RankNet ## See also * [Adapting deep RankNet for personalized search](https://www.shortscience.org/paper?bibtexKey=conf/wsdm/SongWH14)

Summary by Martin Thoma 4 weeks ago
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