1. Web Information retrieval (Web IR)
Handout #12:
Combinational Ranking
Ali Mohammad Zareh Bidoki
ECE Department, Yazd University
Autumn 2011

2. Ranking Algorithm Problems
Rich-get- richer (Connectivity based)
Low precision (at most 0.30)
Each ranking algorithm operates well in some situations
Autumn 2011

3. Combinational Ranking
Content + connectivity +???
How can we combine these features?
R=f( query, content, connectivity)
Autumn 2011

4. Relevance propagation Model (by Shakery)
A hyper score (h) is computed for each document.
WI and WO are weighting functions for in-link and out-link pages, respectively.
S (p) is similarity between query q and page p(self relevance):
Autumn 2011

5. Three Iterative Models
Weighted In-Link
Weighted Out-Link
Uniform Out-Link
Autumn 2011

6. Weighted In-Link
This model of user behavior is quite similar to Random surfer, except that it is not query-independent. The probability that the random surfer visits a page is its hyper-relevance score.
Autumn 2011

7. Weighted Out-Link
In this model, we assume that given a page to a user, he reads the content of the page with probability alpha and he traverses the outgoing edges with probability (1-alpha). The pages that are linked from a page do not have the same impact on its weight.
Pages whose contents are more similar to the query are assumed to have more impact on the score of the page than those which are less similar.
Autumn 2011

8. Uniform Out-Link
In this special case, they assume that at each page, the user reads the content of the page, and with probability (1-alpha) he reads all the pages that are linked from the page.
Autumn 2011

9. Algorithm Implementation
Algorithm is run on a working set
Working set construction:
They first find the top pages which have the highest content similarity to the query
From these pages, a small number (about 200) of the most similar pages are selected to be the core set of pages.
They then expand the core set to the working set by adding the pages that are among the pages and which point to the pages in the core set or are pointed to by the pages in the core set
Autumn 2011

10. Algorithm Properties It is
Online??
Recursive
Query independent
It is shown on TREC Weighted In-Link outperforms others
Autumn 2011

11. Frequency Propagation (By Song)
Instead of Propagation of score, frequency of query terms are propagated
We can use it online
It is used based on site structure
Autumn 2011

12. Propagation Formula ft(p) is the frequency of tem t in page p
f’t(p) is the frequency of tem t in page p after propagation
Autumn 2011

13. Overall Framework for propagation
SS is the best
ST & HT-WI are similar
Autumn 2011

14. Combinational Ranking Algorithms Based on learning (Learning to Rank)
Autumn 2011

15. Combination Framework
Learning
System
q1:{(x11,4),(x12,3),…(x1m,0)}
q2:{(x21,3),(x22,2),…(x2m,1)} ….
qn:{(xn1,4),(xn2,3),…(xnm,2)}
Training Set
Ranking
Model
g(x,w)
Ranking
System
(x1,?), (x2,?),…
Test Set
(x1,g(x1,w))
(x2,g(x2,w))
(x3,g(x2,w)) …
Labels (Relevance judgments or click orders)
Autumn 2011

16. Three learning categories
Point wise
Pair wise
List wise
Autumn 2011