The PageRank algorithm, as used by the Google search engine, exploits the
linkage structure of the web to compute global "importance" scores that
can be used to influence the ranking of search results. While the use of
PageRank has proven very effective, the web's rapid growth in size and
diversity drives an increasing demand for greater flexibility in ranking.
Ideally, each user should be able to define his own notion of importance
for each individual query. While in principle a personalized version of the
PageRank algorithm can achieve this task, its naive implementation requires
computing resources far beyond the realm of feasibility. During 2002-2003,
this project developed algorithms and techniques for the goal of
scalable, online personalized web search. The focus was on the efficient
computation of personalized variants of PageRank.
The members of the Stanford PageRank Project spun off
to form the company Kaltix to commercialize personalized web search
technologies. After a brief life, Kaltix was acquired by
Google in late 2003.
Sepandar D. Kamvar, Taher H. Haveliwala, and Gene H. Golub.
Adaptive Methods for the Computation of PageRank
, Accepted to the International Conference on the Numerical Solution
of Markov Chains, September 2003.
Glen Jeh and Jennifer Widom.
Scaling Personalized Web Search
, In Proceedings of the Twelfth International World Wide Web
Conference, May 2003. (Best Paper Award)
Taher H. Haveliwala,
Topic-Sensitive PageRank
In Proceedings of the Eleventh International World Wide Web Conference,
May 2002. (Best Student Paper Award)
