Abstract
Information overload is an increasingly important concern as users access and generate steadily growing amounts of data. Besides, enterprise applications tend to grow more and more complex which hinders their usability and impacts business users’ productivity. Personalization and recommender systems can help address these issues, by predicting items of interest for a given user and enabling a better selection of the proposed information. Recommendations have become increasingly popular in web environments, with sites like Amazon, Netflix or Google News. However, little has been done so far to leverage recommendations in corporate settings. This paper presents our approach to integrate recommender systems in enterprise environments, taking into account their specific constraints. We present an extensible framework enabling heterogeneous recommendations, based on a semantic model of users’ situations and interactions. We illustrate this framework with a system suggesting structured queries and visualizations related to an unstructured document.
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Thollot, R., Aufaure, MA. (2011). A Framework for Semantic Recommendations in Situational Applications. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20152-3_31
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DOI: https://doi.org/10.1007/978-3-642-20152-3_31
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