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Temporal-Sparsity Aware Service Recommendation Method via Hybrid Collaborative Filtering Techniques

  • Shunmei MengEmail author
  • Qianmu LiEmail author
  • Shiping Chen
  • Shui Yu
  • Lianyong Qi
  • Wenmin Lin
  • Xiaolong Xu
  • Wanchun Dou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11236)

Abstract

Temporal information has been proved to be an important factor to recommender systems. Both of user behaviors and QoS performance of services are time-sensitive, especially in dynamic cloud environment. Furthermore, due to the data sparsity problem, it is still difficult for existing recommendation methods to get the similarity relationships between services or users well. In view of these challenges, in this paper, we propose a temporal-sparsity aware service recommendation method based on hybrid collaborative filtering (CF) techniques. Specifically, temporal influence is considered into classical neighborhood-based CF model by distinguishing temporal QoS metrics from stable QoS metrics. To deal with the sparsity problem, a time-aware latent factor model based on a tensor decomposition model is applied to mine the temporal similarity between services. Finally, experiments are designed and conducted to validate the effectiveness of our proposal.

Keywords

Service recommendation Temporal Sparsity Collaborative filtering CP decomposition 

Notes

Acknowledgment

This paper is partially supported by the National Science Youth Foundation of China under Grant No. 61702264, the Open Research Project of State Key Laboratory of Novel Software Technology (Nanjing University) under Grant No. KFKT2017B07.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shunmei Meng
    • 1
    • 2
    Email author
  • Qianmu Li
    • 1
    Email author
  • Shiping Chen
    • 3
  • Shui Yu
    • 4
  • Lianyong Qi
    • 5
  • Wenmin Lin
    • 6
  • Xiaolong Xu
    • 7
  • Wanchun Dou
    • 2
  1. 1.Department of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  3. 3.CSIRO Data61SydneyAustralia
  4. 4.School of SoftwareUniversity of Technology SydneyUltimoAustralia
  5. 5.School of Information Science and EngineeringQufu Normal UniversityJiningChina
  6. 6.Department of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  7. 7.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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