Query Recommendation for Search Engine Results Using Machine Learning

Abstract

Now a days, web content over the World Wide Web is growing fast, it has become tougher to satisfy the necessities of the client’s queries results. This paper provides a technique for advising a sequence of queries that are similar with the client’s input search. The related searches are based on past given searches by the clients itself. This method tells about the clustering process where syntactical or semantically similar searches in groups are found. This also proposes some queries which are similar or related to the queries submitted by the client to get the required information which is relevant and efficient. This method does not only detect the similar and related searches and queries but can also rank them considering the similarity measure. And this technique is executed using real data sets from search engine query log. It gives queries in websites like Google, yahoo, Bing etc.