Learning to rank
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems
# Resources
- https://en.wikipedia.org/wiki/Learning_to_rank
- Training data consists of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. “relevant” or “not relevant”) for each item. The ranking model’s purpose is to rank, i.e. produce a permutation of items in new, unseen lists in a way which is “similar” to rankings in the training data in some sense.
# Ordinal regression (classification)
- https://en.wikipedia.org/wiki/Ordinal_regression
- OR (also called “ordinal classification” or “ranking learning”) is a type of AI/Supervised Learning/Regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. It can be considered an intermediate problem between regression and classification.
- http://stackoverflow.com/questions/3495157/ordinal-classification-packages-and-algorithms
- http://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/
# Code
- #CODE RAX
- #CODE Adarank
- #CODE Pyltr - LambdaMART
- #CODE Mord - Ordinal Regression in Python