An Integrated Page Ranking Algorithm for Personalized Web Search

Today search engines constitute the most powerful tools for organizing and extracting information from the Web. However, it is not uncommon that even the most renowned search engines return result sets including many pages that are definitely useless for the user. This is mainly due to the fact that the very basic relevance criterions underlying their information retrieval strategies rely on the presence of query keywords within the returned pages. Web Search Personalization is a process of customizing the Web search experience of individual users. The goal of such personalization may range from simply providing the user with a more satisfied results by relevant information. Such a system must be able to deduce the information needs of the user. It is worth observing that statistical algorithms are applied to ―tune‖ the result and, more importantly, approaches based on the concept of relevance feedback are used in order to maximize the satisfaction of user’s needs. Nevertheless, in some cases, this is not sufficient. In this paper search results are ranked based on user preferences in content and link. The preference of content and link is integrated in order to rank the results.