Learning to Rank
A common method to rank a set of items is to pass all items through a scoring function and then sorting the scores to get an overall rank. Traditionally this space has been domianted by ordinal regression techniques on point-wise data. However, there are serious advantages to exploit by learning a scoring function on pair-wise data instead. This technique commonly called RankNet was originally explored by the seminal Learning to Rank by Gradient Descent[^1] paper by Microsoft.
In this talk we will discuss:
Theory behind point-wise and pair-wise data
Ordinal Regression: ranking point-wise data
how to crowd-source pair-wise data