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Topic-Sensitive PageRank. Taher H. Haveliwala Stanford University Presentation by Na Dai. The frame of system using topic-sensitive PageRank. PageRank. Rank is a n-dimension column vector of PageRank values.(i.e. Rank = (Rank(1), Rank(2),…, Rank(n)) T Motivation: irreducible & aperiodic

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topic sensitive pagerank

Topic-Sensitive PageRank

Taher H. Haveliwala

Stanford University

Presentation by Na Dai

pagerank
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)
topic sensitive pagerank 1
Topic-Sensitive PageRank (1)
  • w (w1, w2,…,w16): a normalized vector with length 1
  • wi = Pr(ci|q)
future work
Future Work
  • Investigate the best basis topics
    • Topic granularity
    • Topics that are deeper in hierarchy
  • vj: resistant to adversarial ODP editors