class12 - Topic-specic PageRank Topic Specic Pagerank...

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Topic-specifc PageRank Topic Specifc Pagerank [Have02] Conceptually, we use a random surFer who teleports, with say 10% probability, using the Following rule: Selects a category (say, one oF the 16 top level ODP categories) based on a query & user -specifc distribution over the categories Teleport to a page uniFormly at random within the chosen category Sounds hard to implement: can’t compute PageRank at query time!
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Topic Specifc Pagerank [Have02] Implementation ofFine: Compute pagerank distributions wrt to individual categories Query independent model as beFore Each page has multiple pagerank scores – one For each ODP category, with teleportation only to that category online: Distribution oF weights over categories computed by query context classifcation Generate a dynamic pagerank score For each page - weighted sum oF category-specifc pageranks Infuencing PageRank (“Personalization”) Input: Web graph W in±uence vector v v : (page ! degree oF in±uence) Output: Rank vector r : (page ! page importance wrt v ) r = PR( W , v )
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Non-uniform Teleportation Teleport with 10% probability to a Sports page Sports Interpretation of Composite Score For a set of personalization vectors { v j } " j [w j · PR( W , v j )] = PR( W , " j [w j · v j ]) Weighted sum of rank vectors itself forms a valid rank vector, because PR () is linear wrt v j
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Interpretation Teleport with 10% probability to a Sports page Sports Interpretation Health 10% Health teleportation
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Sports Health pr = (0.9 PR
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class12 - Topic-specic PageRank Topic Specic Pagerank...

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