1. Literature and methods Amir Tavanaei Dr. Vijay Raghavan
RANKING PROJECT
Literature and methods
Amir Tavanaei
Dr. Vijay Raghavan

2. Data Multi-attribute samples Goal: How? Numerical and ordinal
Ranking the samples using
Accumulating,
Partial ordering, or
Learning methods
How?

3. Accumulating Method Each sample is represented by D values:
The simplest method (just to mention)
More advanced:
The problem is to find the weights!

4. Attribute weights Hand-crafted weight values (heuristic) Correlation
Clustering
PCA-Varimax (It needs many pre- and post processing tasks)
Partial Ordering (next slide)
Learning and Regression (using comparable samples)

5. Partial Ordering can Find the Attribute Impact
Partial order investigation for sensitivity analysis*
Method:
Original Hass diagram
Removing attributes one at a time
Finding new Hass diagram
Difference between original Hass diagram and the new one describes the attribute’s importance
*: Paola Annoni, R. Bruggemann, A. Saltelli, 2011
(I have a copy of their book in my documents)

6. Learning To Rank
Learning refers to weight adjustment to show the variable impacts.
Supervised
Classification
Ordinal categories are used
Unsupervised
There is no class label

7. Unsupervised Learning
Unsupervised Learning to rank is the project goal.
What we have:
Dataset containing multi-criteria samples
What we need:
Ranking the samples using unsupervised learning
Probable methods:
Using partially ranked samples
Clustering as pre-processing

8. Project Requirements
An introduction describing unsupervised learning to rank methods.
Following a method and implementing it.
Finding a way (by going through literatures. New ideas are also welcome) to find the ranking performance.
Evaluating the model.
Report

9. Contact Me! What I can give you: I am available:
Papers explaining learning to rank (Supervised and Unsupervised).
Dataset
I am available:
Monday, Thursday, Friday at CVDI Lab, Abdollah Hall
Tuesday, Wednesday, Saturday at CACS, #356