Abstract
The use of the user’s environmental and physical context can reveal important information to enhance Mobile Information Retrieval. However the typical mobile search process integrates all gathered information about the user’s context. These approaches do not take into account user’s intention behind the query, which decreases their reliability and effectiveness in terms of leading to the appropriate user’s information need. In this paper, we study the problem of finding a set of user’s context information allow to disambiguate user’s query. These contextual informations, that we call relevant dimensions, can help to personalize the mobile search process. To this aim we develop a context filtering approach CFA. The problem of finding such set of dimensions can be assimilated to a context filtering problem. We propose a novel measure that directly precises the relevance degree of each contextual dimension, which leads to finally filter the user’s context by retaining only relevant. Our experiments show that our measure can analyze the real user’s context of up to 6,000 of dimensions related to more than 2,000 of user’s 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 filter his context.
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Missaoui, S., Faiz, R. (2014). Adapting User’s Context to Understand Mobile Information Needs. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-319-06740-7_29
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DOI: https://doi.org/10.1007/978-3-319-06740-7_29
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