PERSONALIZING THE SOURCE SELECTION AND THE RESULT MERGING PROCESS

Abstract

The World Wide Web knows an incessant and very fast development. Currently, finding useful information on the Web is a time consuming process. In this paper, we present PIRS a personalized Information Retrieval System in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that has the most relevant content according to the query but ignored the user's specific needs. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and thus, the same query may cover different specific information needs according to who emitted it. However, with the ever expanding Web, users are faced with a huge number of information resources. Consequently, such query-based information access strategies lead to inaccurate query results. PIRS extends the state of the art in a Web-based information retrieval system in distributed environment. First, it develops models for representing both user and information source using feature based profiles. Second, PIRS expands a user query according to his profile. Third, it develops algorithms for source selection and results merging that personalize the computation of the relevance score of a document in response to the user's query. PIRS has been experimented with several known information source. The experimental results obtained show the effectiveness of our approach.