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Learning-to-Rank Workshop/Seminar #1: Summary

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Learning-to-Rank Workshop/Seminar #1: Summary Last month, a Learning-to-Rank (LTR) seminar/workshop was held at RONDHUIT Co, Ltd., at which I gave an overview of the implemented neural network algorithms in the Learning-to-Rank for Lucene (LTR4L) project. More details about the event, including the slides presented by the speakers, can be found here.

The neural network algorithms presented at the workshop were:
 - PRank (Perceptron Ranking)
 - OAP-BPM
 - RankNet
 - FRankNet (Factored RankNet, not RankNet with a fidelity loss function.)
 - LambdaRank
 - SortNet
 - ListNet

In addition to the neural network algorithms, I also gave a brief overview of the following:
 - Loss functions
 - LTR evaluation metrics
 - Gradient Descent/ Stochastic Gradient Descent
 - Back Propagation

Although each of the algorithms have been summarized in the README of LTR4L, as we discussed the algorithms in more detail at the workshop, I would like to elaborate a bit more in this post.
For pseudo-code, please…