Intelligent Web Inference Model for Identifying Interest Changes in Multi User Environment using Web Log Data for Efficient Web Search

Abstract

The modern world spends their maximum time in surfing internet and they perform their most activities through the internet. Also the people learn many things through the internet and for everything they approach web solution. Even though there are many web solutions available, the web search is the most dominant activity performed by many users in their day to day activities. For any web users there will be a concrete interest of topic about which they search through the web search engines but vary with many factors about the user interest changes. There are many frameworks has been discussed earlier for the development of web search using web mining but lags with the prediction and application of web search and user interest. We propose an intelligent inference model which identifies the user interest in various time window and performs an inference about their interest changes. The proposed method consider various factors of web search like the topic, time, actions, navigations, and so on to identify the interest of the users. Based on the above discussed factors, the proposed method identifies the interest changes using the web log data which helps to improve the result of web search. The proposed method adapts the inference results to produce ranked results in many ways and increase the efficiency of web search.