Recommendation algorithm based on improved spectral clustering and transfer learning

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Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating matrixes. In this paper, we propose a recommendation algorithm based on improved spectral clustering and transfer learning (RAISCTL) to improve the forecasting accuracy and generalization ability of recommender system. RAISCTL firstly improves the spectral clustering by using the eigenvalue differences and orthogonal eigenvectors and realizes the automatic determination of cluster numbers. In addition, the improved spectral clustering algorithm is used to cluster the two dimensions of the users and items of the original rating matrix. Then, RAISCTL decomposes the rating matrix after clustering and gets the sharing group rating matrix. Finally, RAISCTL makes rating forecasting and recommendations based on the sharing group rating matrix and transfer learning. The simulation results show that RAISCTL can effectively improve the recommendation accuracy and generalization ability compared with other 8 conventional CF approaches.

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This work is 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), Jiangsu Government Scholarship for Overseas Studies, Jiangsu QingLan Project and Top-notch Academic Programs Project of Jiangsu Higher Education Institutions.

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Correspondence to Xiang Li.

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Li, X., Wang, Z., Hu, R. et al. Recommendation algorithm based on improved spectral clustering and transfer learning. Pattern Anal Applic 22, 633–647 (2019) doi:10.1007/s10044-017-0671-2

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  • Spectral clustering
  • Recommendation algorithm
  • Recommender systems
  • Collaborative filtering
  • Transfer learning