link prediction and path analysis using markov chains (r. r. sarukkai) presentation by h.perrin,...
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Link Prediction and Path Analysis using Markov Chains
(R. R. Sarukkai)
Presentation by H.Perrin, S.Jaffer, S.Lambert & W.Lewis
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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.
The Author: Ramesh R. Sarukkai
● Researches Internet technologies
● Member of W3C committee
● Now works for Yahoo!
The Theory Proposed
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Background: Markov Chains
The Theory
● (INCLUDING MATHS?)
The System
Applications
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HTTP Request Prediction
● In server or proxy
● Allows pre-fetching of most likely pages first
● Connection latency is minimsed
● Server efficiency increases
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
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
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
Experimental Results