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Cluster Computing

, Volume 22, Supplement 1, pp 1151–1167 | Cite as

A new recommendation algorithm combined with spectral clustering and transfer learning

  • Xiang LiEmail author
  • Zhijian Wang
Article

Abstract

Collaborative filtering (CF) recommendation algorithm has been successfully applied into recommender systems for years which can solve the problem of information overload. However, CF suffers from data sparsity and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a collaborative filtering recommendation algorithm combined with spectral clustering and transfer learning (RASCTL). RASCTL firstly uses spectral clustering to cluster the dimensions of users and items in the original rating matrix. In addition, RASCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RASCTL makes rating forecasting and recommendations combined with the sharing group rating matrix and transfer learning. By the simulation experiments on Epinions and MovieLents data sets, the results show that RASCTL is able to obtain comparable or even better recommendation accuracy and generalization ability compared with other seven CF recommendation algorithms.

Keywords

Spectral clustering Recommender systems Collaborative filtering Transfer learning 

Notes

Acknowledgements

This research was financially supported by University Science Research Project of Jiangsu Province (15KJB520004), Science and Technology Projects of Huaian (HAC201601), Science and Technology Project of Jiangsu Province (BE2015127) and Jiangsu Government Scholarship for Overseas Studies.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Research involving animal participants

This article does not contain any studies with animals performed by any of the authors.

Informed consent

It was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer and Information Technology EngineeringHohai UniversityNanjingChina
  2. 2.Faculty of Computer and SoftwareHuaiyin Institute of TechnologyHuaianChina

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