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- W3186240802 abstract "Recently, recommendation systems have gained more popularity, especially in online e-commerce and offline brick-and-mortar businesses such as retailers. In these systems, the users and services transaction data are analyzed to provide reasonable and timely recommendations on products or services to the users. However, with the increasing complexity and volume of processing data, the recommendation system model must be adapted. Edge and cloud computing technologies have been adopted recently in recommendation systems to leverage such systems’ scalability. However, with such a large amount of processing data, efficient data processing techniques are still required. In this paper, we propose a distributed machine learning approach (DE-SVD) for recommendation systems that comprises two steps: a) preliminary data processing on edge servers (edge computing) and b) final data processing on centralized servers (cloud computing). Performance evaluation demonstrates the benefits of our approach in reducing data transfer size between edge servers and cloud servers and training time while preserving the quality of the model (with an insignificant reduction in recommendation performance)." @default.
- W3186240802 created "2021-08-02" @default.
- W3186240802 creator A5046932726 @default.
- W3186240802 date "2021-07-14" @default.
- W3186240802 modified "2023-10-16" @default.
- W3186240802 title "On the Edge and Cloud: Recommendation Systems with Distributed Machine Learning" @default.
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- W3186240802 doi "https://doi.org/10.1109/icit52682.2021.9491121" @default.
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