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Within Mobile information retrieval research, context information provides an important basis for identifying and understanding user’s information needs. Therefore search process can take advantage of contextual information to enhance the query and adapt search results to user’s current context. However, the challenge is how to define the best contextual information to be integrated in search process. In this paper, our intention is to build a model that can identify which contextual dimensions strongly influence the outcome of the retrieval process and should therefore be in the user’s focus. In order to achieve these objectives, we create a new query language model based on user’s pereferences. We extend this model in order to define a relevance measure for each contextual dimension, which allow to automatically classify each dimension. This latter is used to compute the degree of change in result lists for the same query enhanced by different dimensions. Our experiments show that our measure can analyze the real user’s context of up to 12000 of dimensions (related to 4000 queries). We also show experimentally the quality of the set of contextual dimensions proposed, and the interest of the measure to understand mobile user’s needs and to enhance his query.

Missaoui, S., & Faiz, R. (2015). A preferences based approach for better comprehension of user information needs. TRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE, 9240, 67-85 [10.1007/978-3-662-48145-5_4].

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