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

作　　者：Wenmin Lin Min Zhu Xinyi Zhou Ruowei Zhang Xiaoran Zhao Shigen Shen Lu Sun 

机构地区：[1]Alibaba Business School,Hangzhou Normal University,Hangzhou 311121,China [2]Blockchain Laboratory of Agricultural Vegetables,Weifang University of Science and Technology,Shouguang 262700,China [3]School of Computer Science,Qufu Normal University,Rizhao 276827,China [4]School of Information Engineering,Huzhou University,Huzhou 313000,China

出　　处：《Tsinghua Science and Technology》2024年第3期897-910,共14页清华大学学报（自然科学版（英文版）

基　　金：supported by the Natural Science Foundation of Zhejiang Province(Nos.LQ21F020021 and LZ21F020008);Zhejiang Provincial Natural Science Foundation of China(No.LZ22F020002);the Research Start-up Project funded by Hangzhou Normal University(No.2020QD2035).

摘　　要：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.

关 键 词：deep neural collaborative filtering multi-source data cloud-edge collaboration application stackeddenoising auto encoder multiple layer perceptron 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP311.13[自动化与计算机技术—控制科学与工程]