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Tsinghua Science and Technology 2024, 29(3): 897-910 https://doi.org/10.26599/TST.2023.9010050
Open Access | Issue | Published: 04 December 2023

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

Show Author's Information Wenmin Lin1,  Min Zhu2,  Xinyi Zhou1,  Ruowei Zhang3,  Xiaoran Zhao3,  Shigen Shen4( ),  Lu Sun1
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
Keywords:
deep neural collaborative filtering, multi-source data, cloud-edge collaboration application, stacked denoising auto encoder, multiple layer perceptron
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Wenmin Lin1, Min Zhu2, Xinyi Zhou1, Ruowei Zhang3, Xiaoran Zhao3, Shigen Shen4( ), Lu Sun1
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

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 filtering, multi-source data, cloud-edge collaboration application, stacked denoising auto encoder, multiple layer perceptron

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Received: 04 April 2023
Revised: 11 May 2023
Accepted: 24 May 2023
Published: 04 December 2023
Issue date: June 2024

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© The Author(s) 2024.

Acknowledgements

Acknowledgment

This work was supported by the Natural Science Foundation of Zhejiang Province (Nos. LQ21F020021 and LZ21F020008), Zhejiang Provincial Natural Science Foundation of China (No. LZ22F020002), and the Research Start-up Project funded by Hangzhou Normal University (No. 2020QD2035）.

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