topic-sensitive pagerank taher h. haveliwala stanford university presentation by na dai
TRANSCRIPT
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Topic-Sensitive PageRank
Taher H. Haveliwala
Stanford University
Presentation by Na Dai
![Page 2: Topic-Sensitive PageRank Taher H. Haveliwala Stanford University Presentation by Na Dai](https://reader035.vdocument.in/reader035/viewer/2022072005/56649ce05503460f949aa66b/html5/thumbnails/2.jpg)
The frame of system using topic-sensitive PageRank
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PageRank
• Rank is a n-dimension column vector of PageRank values.(i.e. Rank = (Rank(1), Rank(2),…, Rank(n))T
• Motivation: irreducible & aperiodic– Dangling node (Matrix D)
– Damp factor α(Matrix E)
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Topic-Sensitive PageRank (1)
• w (w1, w2,…,w16): a normalized vector with length 1• wi = Pr(ci|q)
p
v1 v2 … … v16
w1 w2 w16
v1i=1/|T1| for i∈T10 else
v2i=1/|T2| for i∈T20 else
v16i=1/|T16| for i∈T160 else
α, M, D, Rank(i)
Rank(i+1)
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Topic-Sensitive PageRank (2)
p
v1 v2
… …
v16
v1i=1/|T1| for i∈T10 else
v2i=1/|T2| for i∈T20 else
v16i=1/|T16| for i∈T160 else
α, M, D, Rank2(i)
Rank2(i+1)
p pα, M, D, Rank1(i)
Rank1(i+1)
α, M, D, Rank16(i)
Rank16(i+1)
Rank
w1 w2 w16
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Effect of ODP-Biasing (1)
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Effect of ODP-Biasing (2)
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Effect of ODP-Biasing (3)
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Query-sensitive Scoring
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Query-sensitive Scoring
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Future Work
• Investigate the best basis topics– Topic granularity– Topics that are deeper in hierarchy
• vj: resistant to adversarial ODP editors