Modeling feature interactions for context-aware QoS prediction of IoT services

Yuanyi CHEN*, Peng YU, Zengwei ZHENG, Jiaxing SHEN, Minyi GUO

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

9 Citations (Scopus)


Accurate Quality of Service (QoS) prediction of service is a key measure to accomplish successful applications such as QoS-aware service recommendation and composition in Internet of Things (IoT) environments. The key of this task is to consider contextual information like geographic location, network address and type of service, since they have subtle but powerful effects on QoS of IoT services. Recently proposed context-aware QoS prediction for IoT services follow two general paradigms: clustering contextual information for calculating similarity between users and services or integrating contextual information by extending latent factor models. However, the simple clustering contextual information or learning the latent feature of contextual information do not go much further to discover complex and intricate user–service interaction patterns. To this end, we propose a context-aware feature interaction modeling (CFM) approach for IoT services to perform QoS prediction, considering context as an additional feature similar to users and services and modeling their interactions. The proposed method can capture both low-order and high-order feature interactions using contextual information and user's invoked records, which consists of three phases: (1) learn low-order feature interactions by decomposing the sparse user–service QoS matrix with factorization machine; (2) learn high-order feature interactions explicitly and implicitly with a multilayer perceptron and deep cross network; (3) aggregate the output of low-order and high-order feature interactions with a parametric-matrix-based fusion. Experimental results on a large-scale QoS dataset demonstrate that the proposed method consistently outperforms state-of-the-art baselines in terms of various evaluation metrics.

Original languageEnglish
Pages (from-to)173-185
Number of pages13
JournalFuture Generation Computer Systems
Early online date4 Aug 2022
Publication statusPublished - Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
Minyi Guo received his B.Sc. and M.E. degrees in computer science from Nanjing University, China, and his Ph.D. degree in computer science from the University of Tsukuba, Japan. He is currently a Zhiyuan Chair Professor and a chair of the Department of Computer Science and Engineering, Shanghai Jiao Tong University, china. He received the National Science Fund for Distinguished Young Scholars award from NSFC in 2007. He is a Fellow of IEEE.

Funding Information:
This work was supported in part by the Top Young Scholar of Zhejiang Province “Ten Thousands Talent Program” , Top Young Scholar of Hangzhou “Ten Thousands Talent Program” , National Natural Science Foundation of China under Grant 62072402 , in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LGN20F020003 , and in part by Hangzhou Science and Technology Bureau under Grant 20191203B37 and the Intelligent Plant Factory of Zhejiang Province Engineering Lab .

Publisher Copyright:
© 2022 Elsevier B.V.


  • Context-aware qoS prediction
  • Deep neural network
  • Feature interaction
  • IoT services


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