Abstract
Recent e-commerce and location-based services provide personalized recommendations based on machine-learning models that take into account purchase and visiting histories. Because machine-learning models assume the same distributions between training and test data, they cannot catch up with concept drifts, i.e., changes of behavioral patterns over time. To keep recommendation accurate, it is important to detect concept drifts. Generally, to achieve this, we need complete data (i.e., data without missing values). In real-world datasets, however, there are many incomplete data, and existing concept drift detection techniques do not deal with incomplete data. To address this issue, we investigate how a deep learning technique (denoising autoencoder), which complements missing values, contributes to detecting concept drifts in incomplete data. We conduct experiments on synthetic and real datasets to evaluate the robustness of this technique, and our results show its advantages.
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This research is supported by JST CREST Grant Number JPMJCR21F2.
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Murao, J., Yonekawa, K., Kurokawa, M., Amagata, D., Maekawa, T., Hara, T. (2022). Concept Drift Detection with Denoising Autoencoder in Incomplete Data. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_35
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DOI: https://doi.org/10.1007/978-3-030-94822-1_35
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