Improving Search Results Through Reducing Replica in User Profile
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Most important visible component of the internet contains millions of web pages waiting to present information on an amazing collection of topics. The search engine has played an increasingly important role. Nowadays, the search engine bases string matching has inherent harms like a low accuracy, inadequate individual support, duplicates document and hyperlinks in the user profile and so on. The present proposed work introduces a method against previously proposed personalized query clustering method in our research. The Experimental results show that a profile captures and utilize both user’s replica and non replicated preferences. The non replica of hyper, sub-hyperlinks and cluster content performs the best of among results. An important result from the experiments is that profiles with replica of links can increase the separation between similar and different queries. The separation provides a clear threshold for a LINGO clustering algorithm to terminate and improve the overall quality of the resulting query clusters and hyper, sub-hyper links
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