9 Algorithms: PageRank. Ranking After matching, have to rank:

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Presentation transcript:

9 Algorithms: PageRank

Ranking After matching, have to rank:

Index Based Ranking Strategies we could (do) use: – Frequency – Position – Metadata

Missing Ingredient Index lacks intra-page information

Link Quality Not all links are equal Who do you trust? – CS Prof – World Famous Chef

Identifying Authority Links into a page give it authority Page value = sum of authorities of pages linking to it

Link Quality More links is easy to abuse Spam Link Pages

Issues Spam Links – Discourage with negative weight Spam Link Pages

Issues Cycles:

Issues Cycles:

Issues Cycles: …

Random Surfer Simulating a web surfing session – Start at random page – At each page have a chance to Pick a random link to go to Jump to a completely random page

Results Results of many random sessions:

Results Expressed as percentages, results stabilize – Law of large numbers

Cycle Buster Random surfer not phased by cycles:

Random Surfer In Use The recipe pages visited by random surfers:

Simulator PageRank Simulator:

The Real Math Markov Chains – Set of states – Each state has probability of leading to other states – Represent as matrix

Excel Simulation Three pages:

Limitations Still have issues/room for growth – Link Spam – Context of link Where link is on page "Bob's recipe is terrible" vs "Bob's recipe is great" – Lack of semantic knowledge Page's Authority should not be the same for all domains

Power Controlling search is power: "If you're not paying for the product, you are the product."