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Published byBrooke Byrd Modified over 11 years ago
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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 1
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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.
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The Author: Ramesh R. Sarukkai
Researches Internet technologies Member of W3C committee Now works for Yahoo!
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The Theory Proposed 4
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Background: Markov Chains
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The Theory (INCLUDING MATHS?)
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The System
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Applications 8
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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
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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
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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
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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
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Experimental Results
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