Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

Publication Date

3-1-2024

Document Type

Article

Publication Title

IEEE Transactions on Broadcasting

Volume

70

Issue

1

DOI

10.1109/TBC.2023.3278111

First Page

148

Last Page

160

Abstract

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE.

Keywords

Digital multimedia broadcasting, E-commerce, Electronic commerce, higher-order tensor singular-value-decomposition (HOTSVD), Matrix decomposition, Multimedia systems, normalized root-mean-square error (NRMSE), parallel computation, Personalized multimedia recommendation system (PMRS), Recommender systems, Singular value decomposition, Tensors

Department

Applied Data Science

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