Topic-Sensitive PageRank, introduced by Haveliwala (WWW 2002; extended in IEEE TKDE 2003), is a seeded variant of PageRank in which the uniform teleport distribution $v$ is replaced by a topic-biased distribution $v_T$ that puts mass only on a chosen seed set of pages representative of a topic $T$. For each topic in a fixed set of representative topics (e.g., ODP top-level categories), a biased PageRank vector

$r_T = (1 - \alpha)\, M\, r_T + \alpha\, v_T$

is precomputed. At query time, the query's topic distribution is estimated, and the corresponding biased PageRank vectors are mixed to produce a query-sensitive importance score. The method is the canonical seeded/personalized PageRank construction: graph diffusion from a seed set through the link structure, with the seed encoded in the teleport vector. It is the propagation primitive that dense-seeded local diffusion retrieval methods generalize, replacing stationary random-walk scores with alternative aggregation rules over reaching paths.