Abstract
When computer programs participate in conversations, they can learn things about the people they are conversing with. A conversational system that helps a user select a flight may notice that a person prefers a particular seating arrangement or departure airport. In this paper we discuss a system which uses the information state accumulated during a person-machine conversation and a case-based analysis to derive preferences for the person participating in that conversation. We describe the implementation of this system based on a MapReduce framework that allows for near real-time generation of a user’s preferences regardless of the total case memory size. We also show some preliminary performance results from scaling tests.
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Beaver, I., Dumoulin, J. (2013). Applying MapReduce to Learning User Preferences in Near Real-Time. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_2
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DOI: https://doi.org/10.1007/978-3-642-39056-2_2
Publisher Name: Springer, Berlin, Heidelberg
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