@article{Lin2024, 
author = {Wenmin Lin and Min Zhu and Xinyi Zhou and Ruowei Zhang and Xiaoran Zhao and Shigen Shen and Lu Sun},
title = {A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications},
year = {2024},
journal = {Tsinghua Science and Technology},
volume = {29},
number = {3},
pages = {897-910},
keywords = {deep neural collaborative filtering, multi-source data, cloud-edge collaboration application, stacked denoising auto encoder, multiple layer perceptron},
url = {https://www.sciopen.com/article/10.26599/TST.2023.9010050},
doi = {10.26599/TST.2023.9010050},
abstract = {Service recommendation provides an effective solution to extract valuable information from the huge and ever-increasing volume of big data generated by the large cardinality of user devices. However, the distributed and rich multi-source big data resources raise challenges to the centralized cloud-based data storage and value mining approaches in terms of economic cost and effective service recommendation methods. In view of these challenges, we propose a deep neural collaborative filtering based service recommendation method with multi-source data (i.e., NCF-MS) in this paper, which adopts the cloud-edge collaboration computing paradigm to build recommendation model. More specifically, the Stacked Denoising Auto Encoder (SDAE) module is adopted to extract user/service features from auxiliary user profiles and service attributes. The Multiple Layer Perceptron (MLP) module is adopted to integrate the auxiliary user/service features to train the recommendation model. Finally, we evaluate the effectiveness of the NCF-MS method on three public datasets. The experimental results show that our proposed method achieves better performance than existing methods.}
}