article

Recommendation algorithm based on improved spectral clustering and transfer learning

Authors:

Liuyang WangAuthors Info & Claims

Pattern Analysis & Applications, Volume 22, Issue 2

Pages 633 - 647

https://doi.org/10.1007/s10044-017-0671-2

Published: 01 May 2019 Publication History

Abstract

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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Published In

cover image Pattern Analysis & Applications

Pattern Analysis & Applications Volume 22, Issue 2

May 2019

464 pages

ISSN:1433-7541

EISSN:1433-755X

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Copyright © Copyright © 2019 Springer-Verlag London Ltd., part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2019

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Cited By

Khan AAkhter MMaheshwari RMohanty S(2024)L-ASCRA: A Linearithmic Time Approximate Spectral Clustering Algorithm Using Topologically-Preserved RepresentativesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348357236:12(8643-8654)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3483572
Motallebi HNasihatkon RJamshidi M(2022)A local mean-based distance measure for spectral clusteringPattern Analysis & Applications10.1007/s10044-021-01040-525:2(351-359)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10044-021-01040-5
Kai H(2021)Automatic Recommendation Algorithm for Video Background Music Based on Deep LearningComplexity10.1155/2021/66969862021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6696986
Li XWang ZGao SHu RZhu QWang L(2019)An Intelligent Context-Aware Management Framework for Cold Chain Logistics DistributionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.288906920:12(4553-4566)Online publication date: 20-Dec-2019
https://dl.acm.org/doi/10.1109/TITS.2018.2889069

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