De motor van de zoekrobot Bing = het RankNet algoritme
 

Microsoft ontwikkelde zelf een zoekrobot: BING.

RankNet algoritme werd ontwikkeld.
Vervolgens kwam LambdaRank.
En LambdaMART.

= LTR algoritmes ontwikkeld door Chris Burges en collega's [Microsoft Research]

What is Learning to Rank?
Learning to Rank (LTR) is a class of techniques that apply supervised machine learning (ML) to solve ranking problems. The main difference between LTR and traditional supervised ML is this:

• Traditional ML solves a prediction problem (classification or regression)
  on a single instance at a time. E.g. if you are doing spam detection on email,
  you will look at all the features associated with that email and classify it as spam or not.
  The aim of traditional ML is to come up with a class (spam or no-spam)
  or a single numerical score for that instance.
• LTR solves a ranking problem on a list of items.
  The aim of LTR is to come up with optimal ordering of those items.
  As such, LTR doesn’t care much about the exact score that each item gets,
  but cares more about the relative ordering among all the items.

The most common application of LTR is search engine ranking, but it’s useful anywhere you need to produce a ranked list of items.
BRON

Een schets van wat er gebeurt. Een model wordt getraind met data.
Dat levert een systeem op dat kan getest worden.
Figuur uit:

Intuitive explanation of Learning to Rank (and RankNet, LambdaRank and LambdaMART)
_{14 jan 2016}