Entropy a new measure to gauge search engine optimisation

S. Lakshmi, B. Sathiyabhama and K. Batri

International Journal of Enterprise Network Management, 2018, vol. 9, issue 3/4, 189-204

Abstract: This article tries to analyse, and measure the uncertainty associated with the relevant document selection in web-search engines. The number of index terms, and their occurrence frequency influences the relevance calculation. The variation in term frequency either in processed web documents or in users' query influences the relevance calculation. This leads to an uncertainty associated with the document selection, and it is relevance calculation. In this article, we proposed a new measure called entropy. The entropy can be measured by varying the documents' term frequency or user's query term frequency. As the web documents can't be changed, we used variation in user's query term frequency to measure the uncertainty associated with the document selection in web-search engines. The query's term frequency is varied and given to the search engines. namely 'Google', and 'Bing' The high uncertainty gives scope for search engine optimisation. From the high uncertainty search engines, we can extract more relevant documents.

Keywords: web search engines; uncertainty; entropy; document selection; Google; Bing; search engine optimisation. (search for similar items in EconPapers)
Date: 2018
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