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– Dangling node (Matrix D)

– Damp factor α(Matrix E)

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)

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

Effect of ODP-Biasing (1)

Effect of ODP-Biasing (2)

Effect of ODP-Biasing (3)

Query-sensitive Scoring

Query-sensitive Scoring

Future Work

• Investigate the best basis topics– Topic granularity– Topics that are deeper in hierarchy

• vj: resistant to adversarial ODP editors