1. Web Information retrieval (Web IR)
Handout #13:
Ranking based on User Behavior
Ali Mohammad Zareh Bidoki
ECE Department, Yazd University
Autumn 2011

2. Finding Ranking Function
R=f( Query, User behavior, web graph & content features)
How can we use the user behavior?
Explicit
Implicit
80% of user clicks are related to query
Click-through data
From search Engines log
بهترین معیار برای ترکیب رفتار کاربر است. چون در نهایت رضایت کاربر مهم است.
Autumn 2011

3. Click-through data (by Joachims )
Triple (q,r,c)
q=query
r=ranked list
c=set of clicked docs c q r
Autumn 2011

4. Benefits of Using Click through data
Democracy in Web
Filling gap between user needs and results
User clicks are more valuable that a page content (Search engine precision is evaluated by user no page creators)
Degree of relevancy between query and documents will increase (Adding click metadata to document)
Autumn 2011

5. Web Entities 1 2 n Web graph 1 2 n 1 2 w 1 2 q 1 2 m Docs Docs Words
Queries 1 2 m
Users
Autumn 2011

6. Document Expansion Using Click TD
First time Google used Anchortext as a document content
Anchor text is view of a document from another document
Autumn 2011

7. Long term incremental learning
Di vector of a document in ith iteration
Q is vector of the query that this document is clicked
Alpha is learning rate
Autumn 2011

8. Naïve Method (NM) A bipartite graph for docs and queries
Mij is number of clicks on document j for query i
Autumn 2011

9. Naïve Method (Cont.) The weight between query qj and document di:
The meta data for document i is:
Autumn 2011

10. Co-Visited Method
If two pages are clicked by the same query they called co-visited.
The similarity between two docs i and j is (visited(di) shows number of clicks on di and visited(di,dj) shows number of queries in which both are clicked):
Autumn 2011

11. Co-Visited Disadvantages
It only considers documents similarity (not query similarity)
As users clicks on top 10 pages, click data are sparse (1.5 queries for each page)
So similarity is not precise
Autumn 2011

12. Iterative Method (IM) O(q): set of clicked page for q
Oi(q): the ith clicked page for q
I(d): set of queries in which it is clicked on d
Ii(d): The ith query in which it is clicked on d
Autumn 2011

13. Experimental Results
Experimental results on a real large query click-through log, i.e. MSN query log data, indicate that the proposed algorithm relatively outperforms
the baseline search system by 157%,
naïve query log mining by 17% and
co-visited algorithm by 17%
on top 20 precision respectively.
Autumn 2011