Context-Aware QoS Prediction with Neural Collaborative Filtering for Internet-of-Things Services

Honghao Gao, Yueshen Xu*, Yuyu Yin, Weipeng Zhang, Rui Li, Xinheng Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

183 Citations (Scopus)


With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

Original languageEnglish
Article number8918285
Pages (from-to)4532-4542
Number of pages11
JournalIEEE Internet of Things Journal
Issue number5
Publication statusPublished - May 2020


  • Contextual information
  • Internet of Things (IoT)
  • Quality-of-Service (QoS) prediction
  • fuzzy clustering
  • neural collaborative filtering (NCF)

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