Search Engine

Search Engine

Abstract— Search Engine Web search engine provides information for the submitted query of the users, without consideration of user’s interests. Personalized Web search is used to consider the user interests for providing the results. Existing research Link-click-concept based ranking (LC2R) algorithm is suggested that extracts a user’s conceptual preferences from users’ click through data resulted from web search. This preference is used to rank the results in a search engine. But the location effects of the users are taken into consideration. In this manuscript, an innovative technique is introduced called Location-based Ranking Method (LBRM) for Ranking Search Results in the search engine. The users have to search at different locations and acquire different search results. This method consists of three phases: Similarity identification, Computation of frequent-access pattern and Weighted score computation. In the similarity identification phase, the location and page similarity is identified by computing similarity among the locations and retrieval pages. In the computation of frequent-access pattern, < Final Year Projects > find all the frequent-retrieval of the web pages by computing the support value. The Pattern consists of Query, Location of the user and retrieved pages. Weighted score computation phase computes the weighted score for the patterns and rank the results based on the highest score. An experimental result shows that the proposed method achieves high efficiency in terms of precision and recall.