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Open Access

A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications

Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China
Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Shouguang 262700, China
School of Computer Science, Qufu Normal University, Rizhao 276827, China
School of Information Engineering, Huzhou University, Huzhou 313000, China
Show Author Information

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.

Keywords

deep neural collaborative filteringmulti-source datacloud-edge collaboration applicationstacked denoising auto encodermultiple layer perceptron

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Tsinghua Science and Technology
Volume 29 Issue 3,
June 2024
Pages 897-910
DOI: 10.26599/TST.2023.9010050
Cite this article:
Lin W, Zhu M, Zhou X, et al. A Deep Neural Collaborative Filtering Based Service Recommendation Method with Multi-Source Data for Smart Cloud-Edge Collaboration Applications. Tsinghua Science and Technology, 2024, 29(3): 897-910. https://doi.org/10.26599/TST.2023.9010050

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Received: 04 April 2023
Revised: 11 May 2023
Accepted: 24 May 2023
Published: 04 December 2023
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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