document: Scaling personalized web search Recent web search techniques augment traditional text matching with a global notion of `` importance '' based on the linkage structure of the web , such as in Google 's PageRank algorithm . For more refined searches , this global notion of importance can be specialized to create personalized views of importance -- for example , importance scores can be biased according to a user-specified set of initially-interesting pages . Computing and storing all possible personalized views in advance is impractical , as is computing personalized views at query time , since the computation of each view requires an iterative computation over the web graph . We present new graph-theoretical results , and a new technique based on these results , that encode personalized views as partial vectors . Partial vectors are shared across multiple personalized views , and their computation and storage costs scale well with the number of views . Our approach enables incremental computation , so that the construction of personalized views from partial vectors is practical at query time . We present efficient dynamic programming algorithms for computing partial vectors , an algorithm for constructing personalized views from partial vectors , and experimental results demonstrating the effectiveness and scalability of our techniques .
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