An Integrating Approach by Using FP Tree and Genetic Algorithm for Inferring User Interesting Results

摘要

Search engine optimization is an interesting research issue in information retrieval for retrieving user interesting results. Satisfying the user search goal is a complex task while searching user specific query, because of billions of related and unrelated data available over the network. In this proposed work we are introducing an empirical model of search mechanism with FP Tree for finding frequent use of patterns (sequence of Urls) and genetic algorithm, which belongs to the larger class of evolutionary algorithms, which will be used for generating solutions for optimization problems using techniques such as mutation and crossover for optimal results with efficient feedback sessions, based on query clicks. From the time user starts clicking or visiting the urls the session is started and the session of feedback is generated reflecting the efficient user information needs.