Topic-Sensitive PageRank
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Revision as of 05:01, 6 March 2021 by imported>Jworkorg (correction)
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 search engine results pages, although no evidence has been shown of it in practice.[citation needed]
Algorithm
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
References
- ↑ Haveliwala, Taher (2002). "Topic-Sensitive PageRank". Proceedings of the Eleventh International World Wide Web Conference (Honolulu, Hawaii). http://infolab.stanford.edu/~taherh/papers/topic-sensitive-pagerank.pdf.
- ↑ Haveliwala, Taher (2003). "Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search". IEEE Transactions on Knowledge and Data Engineering. http://infolab.stanford.edu/~taherh/papers/topic-sensitive-pagerank-tkde.pdf.
Further reading
- Haveliwala, Taher; Jeh, Glen; Kamvar, Sepandar (2003). "An Analytical Comparison of Approaches to Personalizing PageRank". Stanford University Technical Report. http://infolab.stanford.edu/~taherh/papers/comparison.pdf.
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