Abstract
The increase of mobile computing systems and the growth of mobile Web highly influence the needs for personalized mobile browsers. To this aim, mobile applications take benefits from context-aware computing by adapting search process through user's context information (parameters). However, Contextualized Mobile Information Retrieval still remains a challenging problem. This last is to identify contextual parameters that improve search effectiveness and should therefore be in the user's focus. We investigate in this paper the problem of filtering mobile user's context and we propose a Language model for relevant context fields recognition. The proposed model interprets a context field relevance as a metric measure and estimates it using different features. In particular, we propose to filter as much as possible the mobile user's context to emanate efficient information that help in a personalization access to information.person- alized mobile browsers.
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