An Effective Approach for Increasing the Efficiency of Web Searching With Feedback Sessions

There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, one such algorithm called “Classified Average Precision”, was introduced to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate ranking will be done automatically. In this paper we generate an algorithm called “A Fuzzy Self Constructing Algorithm” for clustering the feedback sessions. Mostly fuzzy logic is used for clustering the data sets. The proposed algorithm significantly reduces the computation time required to partition the dataset. It will reduce the original data set in to simplified dataset. It simplifies the data set and find relevant documents based on user feedback sessions. This will automatically iterate every time and reduce the number of iterations while speeding up the calculations and improve the run time performance. Keywords— Feed Back Sessions, Click Rate Ranking, and Fuzzy Self Constructing Algorithm