Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition-Reference-Cited by-同舟云学术

Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Published:2023-06-14 Issue: Volume: Page:
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Container-title:2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
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Author:

Chang Shih Yu¹,Wu Hsiao-Chun²,Yan Kun³,Chen Xinjia⁴,Wu Yiyan⁵

Affiliation:

1. San Jose State University,Department of Applied Data Science,San Jose,CA,USA,95192

2. Louisiana State University,School of Electrical Engineering and Computer Science,Baton Rouge,LA,USA,70803

3. Guilin University of Electronic Technology,Guangxi Key Laboratory of Wireless Wideband Communication, and Signal Processing,Guilin,China,541004

4. Northwestern State University of Louisiana,Department of Engineering Technology,Natchitoches,LA,USA,71497

5. Communications Research Centre Canada,Ottawa,ON,Canada,K2H 8S2

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10210852/10211094/10211188.pdf?arnumber=10211188

Reference17 articles.

1. Literature survey on low rank approximation of matrices

2. Incremental singular value decomposition algorithms for highly scalable recommender systems;sarwar;Proc of the Fifth International Conference on Computing and Information,2002

3. Convex tensor decomposition via structured schatten norm regularization;tomioka;Advances in neural information processing systems,2013

4. A multilinear singular value decomposition;de lathauwer;SIAM Journal on Matrix Analysis and Applications,2000

5. Application of Dimensionality Reduction in Recommender System - A Case Study

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