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…

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