User profile creation based on navigation pattern for modeling user behaviour with personalised search

Abstract

Web Usage Mining (WUM) is the process of extracting knowledge from Web users access data by exploiting Data Mining techniques. It mines the secondary data (web logs) derived from the user interaction with the web pages during certain period of Web sessions. In the present research work, a hybrid method is proposed, which uses the ant-based clustering and LCS classification methods to find and predict user's navigation behaviour. Thus user profile can be tracked in dynamic pages. Personalized search can be used to address challenge in the web search community, based on the premise that a user's general preference may help the search engine disambiguate the true intention of a query. Each user can view searching history along with ranking. In this paper, how a search engine can learn a user's preference automatically based on area of interest and how it can use the user preference to personalize search results.