Personalizing Information Retrieval in a Distributed Environment

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Information personalization is the process of enhancing an information seeking process with user preferences in order to provide accurate results in response to a user query. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and so a same query may cover different specific information needs according to who emitted it. In this paper, we address information personalization in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that have the most relevant content according to the query but ignored the user’s specific needs. 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. In order to tackle this problem, we propose (a) models for representing both user and information source using feature based profiles (b) 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. The proposed personalization approach has been experimented using a multi-agent based framework dealing with several known search engines. The experimental results obtained show the effectiveness of our approach.
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Distributed Information Retrieval; User Profile; Source Profile; Source Selection; Result Merging; Multi-Agent System

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