Improving Search Results with Data Mining in a Thematic Search Engine

The problem of obtaining relevant results in web searching has been tackled with several approaches. Although very e0ective techniques are currently used by the most popular search engines when no a priori knowledge on the user's desires beside the search keywords is available, in di0erent settings it is conceivable to design search methods that operate on a thematic database of web pages that refer to a common body of knowledge or to speci3c sets of users. We have considered such premises to design and develop a search method that deploys data mining and optimization techniques to provide a more signi3cant and restricted set of pages as the 3nal result of a user search. We adopt a vectorization method based on search context and user pro&le to apply clustering techniques that are then re3ned by a specially designed genetic algorithm. In this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments that has been run on test sets of web pages.