Topic-Sensitive PageRank

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Topic-Sensitive PageRank (commonly referred to as TSPR) is a context-sensitive ranking algorithm for web search developed by Taher Haveliwala while at Stanford University, [1] [2] and thought to be used by Google for the purpose of indexing and ranking search results in the SERPs, although no evidence has been shown of it in practice.[citation needed]



Topic-Sensitive PageRank is based on the PageRank algorithm, and provides a scalable approach for personalizing search rankings using Link analysis.

Related Resources

  • Taher Haveliwala's slides describing the Topic-Sensitive PageRank algorithm

See also


  1. Haveliwala, Taher (2002). "Topic-Sensitive PageRank". Proceedings of the Eleventh International World Wide Web Conference (Honolulu, Hawaii). 
  2. Haveliwala, Taher (2003). "Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search". IEEE Transactions on Knowledge and Data Engineering. 

Further reading

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