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 Descent1 paper by Microsoft.
In this talk we will discuss:
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Theory behind point-wise and pair-wise data
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Ordinal Regression: ranking point-wise data
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how to crowd-source pair-wise data
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RankNet: siamiese architecture for ranking pair-wise data
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Case Study: Image Ranking & Search Engine Query Results Ranking
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Case Study: LTR on Search Engine Clickstream data
Footnotes
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Burges, Chris, et al. "Learning to rank using gradient descent." Proceedings of the 22nd international conference on Machine learning. ACM, 2005. ↩
hello! did you ever write a RankNet example? I m looking for examples as I m trying to create a ranking model for tasks. I would appreciate any leads. thanks!