Abstract
Users are finding themselves interacting with increasingly complex software systems and expanding information resources. However many of these systems have little to no awareness of the personally-understood user context which expresses why they are being used. In this paper we propose a framework for modelling and proactively retrieving previously accessed and created information objects and resources that are within the context of a user’s current situation. We first consider theories of context to understand the discrete aspects of context that may delineate a user’s composite situations. With this we develop a framework for modelling user interaction in context along with a re-configurable algorithm for making personal recommendations for desired information objects based upon the environmental, content-based and task sequence contextual similarity of the current situation to past situations. To measure the effectiveness of our approach we use a two week activity log from four real users in a preliminary lab-based evaluation methodology. Initial results suggest the framework as a static personal recommendation algorithm is effective to varying degrees during periods of interaction for users of various characteristics.
Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval
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Whiting, S., Jose, J. (2011). Context Modelling for Situation-Sensitive Recommendations. In: Christiansen, H., De Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2011. Lecture Notes in Computer Science(), vol 7022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24764-4_33
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DOI: https://doi.org/10.1007/978-3-642-24764-4_33
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