Improving Search Strategy of Search Engine Using Probabilistic Latent Semantic

Users on the internet uses search engine to find information of their interest. However current search engines on web return answer to a query of user independent of user’s requirement for the information. In this paper our aim is to use a new technique called probabilistic latent s accurate than previously used techniques by various search engines. Our main focus in this paper is on the requirement for more accurate search results by meta search engine. In comparison w for searching like LSA, which perform singular value decomposition of co this paper, relies on mixture decomposition derived from latent class model. Results obtained by PLSA in searc for query shows that this technique gives more accurate results in searching most relevant document from a given corpus for a query of user