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.
