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Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different(More)
In the context of biomedical information retrieval (IR), this paper explores the relationship between the document's global context and the query's local context in an attempt to overcome the term mis-match problem between the user query and documents in the collection. Most solutions to this problem have been focused on expanding the query by discovering(More)
In the context of document retrieval in the biomedical domain, this paper introduces a novel approach to searching for biomedical information using contextual semantic information. More specifically, we propose to combine the contextual semantic information in documents and user queries in an attempt to improve the performance of biomedical information(More)
Most of the previous approaches surrounding collaborative information retrieval (CIR) provide either a user-based mediation, in which the system only supports users' collaborative activities, or a system-based mediation, in which the system plays an active part in balancing user roles, re-ranking results, and distributing them to optimize overall retrieval(More)
A key challenge in information retrieval is the use of contex-tual evidence within the ad-hoc retrieval. Our contribution is particularly based on the belief that contextual retrieval is a decision making problem. For this reason we propose to apply influence diagrams which are extension of Bayesian networks to such problems, in order to solve the hard(More)
It is well known that with the increasing of information volumes across the Web, it is increasingly difficult for search engines to deal with ambiguous queries. In order to overcome this limit, a key challenge in information retrieval nowadays consists in enhancing an information seeking process with the user's context in order to provide accurate results(More)
Within the information overload on the web and the diversity of the user interests, it is increasingly difficult for search engines to satisfy the user information needs. Personalized search tackles this problem by considering the user profile during the search. This paper describes a personalized search approach involving a semantic graph-based user(More)
The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs. We present in this paper a personalized document ranking(More)
It is now widely assumed in personalized information retrieval (IR) area that user interests can provide substantial clues for document relevance estimation. User interests reflect generally the user background and topics of interests. However most of the proposed personalized retrieval models and strategies do not distinguish between short term and long(More)