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9 Algorithms: PageRank.

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Presentation on theme: "9 Algorithms: PageRank."— Presentation transcript:

1 9 Algorithms: PageRank

2 Ranking After matching, have to rank:

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

4 Missing Ingredient Index lacks intra-page information

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

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

7 Issues Cycles:

8 Issues Cycles:

9 Issues Cycles:

10 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

11 Results Results of many random sessions:

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

13 Cycle Buster Random surfer not phased by cycles:

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

15 Simulator PageRank Simulator:

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

17 Excel Simulation Three pages:

18 Link Quality More links is easy to abuse Spam Link Pages

19 Issues Spam Links Discourage with negative weight Spam Link Pages -1

20 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

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


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