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]
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 and 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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