Abstract
In e-commerce applications, no systematic research has been provided to evaluate if the use of a detailed and rich contextual representation improves the user modeling predictive performances. An underestimated issue is also evaluating if context could be inferred by existing customer data off-line, in spite of getting the customer involved on-line in the gathering process. In this paper, we address those problems, defining context as “the intent of” a customer purchase. To this aim, we collected data containing rich contextual information, hierarchically structured, by developing a special-purpose browser. The experimental results show that the finer the granularity of contextual information the better is the modeling of customers’ behavior. Representing the context in a hierarchical structure is a necessary condition, for inferring the context off-line, but it’s not a sufficient one.
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Palmisano, C., Tuzhilin, A., Gorgoglione, M. (2007). User Profiling with Hierarchical Context: An e-Retailer Case Study. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds) Modeling and Using Context. CONTEXT 2007. Lecture Notes in Computer Science(), vol 4635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74255-5_28
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DOI: https://doi.org/10.1007/978-3-540-74255-5_28
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