Improved Web Search Engine by New Similarity Measures

Information retrieval is a process of managing the user's needed information. IR system captures dynamically crawling items that are to be stored and indexed into repositories; this dynamic process facilitates retrieval of needed information by search process and customized presentation to the visualization space. Search engines plays major role in finding the relevant items from the huge repositories, where different methods are used to find the items to be retrieved. The survey on search engines explores that the Naive users are not satisfying with the current searching results; one of the reason to this problem is "lack of capturing the intention of the user by the machine". Artificial intelligence is an emerging area that addresses these problems and trains the search engine to understand the user's interest by inputting training data set. In this paper we attempt this problem with a novel approach using new similarity measures. The learning function which we used maximizes the user's preferable information in searching process. The proposed function utilizes the query log by considering similarity between ranked item set and the user's preferable ranking. The similarity measure facilitates the risk minimization and also feasible for large set of queries. Here we have demonstrated the framework based on the comparison of performance of algorithm particularly on the identification of clusters using replicated clustering approach. In addition, we provided an investigation analysis on clustering performance which is affected by different sequence representations, different distance measures, number of actual web user clusters, number of web pages, similarity between clusters, minimum session length, number of user sessions, and number of clusters to form.