Scaling User Preference Learning in Near Real-Time to Large Datasets
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چکیده
In previous research we have shown the architecture and application of a case-based reasoning (CBR) system used to discover user preferences in an existing mixed-initiative dialogue system. In this paper we apply this CBR system to increasingly large datasets to test its ability to maintain nearreal time performance in generating new user preferences. We also propose possible future applications of the system.
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تاریخ انتشار 2014