Note: The members of the Stanford PageRank Project have recently spun off to form Kaltix (pronounced call-ticks), a company to commercialize personalized web search technologies. The PageRank algorithm, 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. In the past couple of years, we have developed algorithms and techniques towards the goal of scalable, online personalized web search. Our focus is on the efficient computation of personalized variants of PageRank.