Kernel Expansion for Online Preference Tracking
نویسندگان
چکیده
User preferences of music genres can significantly changes over time depending on fashions and the personal situation of music consumers. We propose a model to learn user preferences and their changes in an adaptive way. Our approach refines a model for user preferences by explicitly considering two plausible constraints of computational costs and limited storage space. The model is required to adapt itself to changing data distributions, and yet be able to compress “historical” data. We exploit the success of kernel SVM, and we consider an online expansion of the induced space as a preprocessing step to a simple linear online learner that updates with maximal agreement to previously seen data.
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