concept set extraction with user session context
The rapid increase of the information on the World Wide Web (WWW) makes it challenging to extract the relevant information utilizing the reasonable amount of resources. Most of the time, it become necessary for users to modify their search queries several times before they obtain the necessary information. Certainly there are various ways to circumvent this problem up to a certain extent. However, most of those approaches do not consider the searching behavior of the end users. A wide spectrum of research is on its way to personalize the web search results to meet the user needs. Therefore, we are proposing an algorithm to generate a hierarchical structure to extract the general user search patterns identified based on the randomly selected seed word and queries. In other words, we propose grouping user queries semantically to form what we refer to as "super concepts" composed of related queries. Our results indicate significant improvement in retrieval effectiveness by utilizing the generic behavioral patterns.
information searching behavior concept extraction user search session