1. Link Prediction and Path Analysis using Markov Chains
(R. R. Sarukkai)
Presentation by H.Perrin, S.Jaffer, S.Lambert & W.Lewis 1

2. Link Prediction & Path Analysis
Volume of pages makes efficient WWW navigation difficult
Aim: To analyse users' navigation history to generate tools that increase navigational efficiency
ie. Predictive server prefetching
Provides tools for other work previously done.

3. The Author: Ramesh R. Sarukkai
Researches Internet technologies
Member of W3C committee
Now works for Yahoo!

4. The Theory Proposed 4

5. Background: Markov Chains

6. The Theory
(INCLUDING MATHS?)

7. The System

8. Applications 8

9. HTTP Request Prediction
In server or proxy
Allows pre-fetching of most likely pages first
Connection latency is minimsed
Server efficiency increases

10. Link Suggestion (Adaptive Web Navigation)
Link prediction used to offer links to users based on previous navigation history
Similar technique has been applied (ie. Amazon)
But not necessarily using Markov Chains
Can be client or server side

11. Tour Generation Given a start URL, user guided along path of links
Appropriate to user's interests
Sequentially pick the next most popular link
Non-cyclic

12. Hub/Authority Identification
Kleinberg proposed Web 'Hub/Authorities'
Hub: Web site that is a good starting point for finding identification
Authority: Web site that contains useful information on a particular topic.
Hub/Authority weighting given in Markov transitional weighting

13. Experimental Results