SVD-based incremental approaches for recommender systems

Zhou,X, He,J, Huang,G and Zhang,Y 2015, SVD-based incremental approaches for recommender systems, Journal of computer and system sciences, vol. 81, no. 4, pp. 717-733, doi: 10.1016/j.jcss.2014.11.016.

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Title SVD-based incremental approaches for recommender systems
Author(s) Zhou,X
Huang,GORCID iD for Huang,G
Journal name Journal of computer and system sciences
Volume number 81
Issue number 4
Start page 717
End page 733
Total pages 17
Publisher Academic Press
Place of publication Maryland Heights, MO
Publication date 2015-06
ISSN 0022-0000
Keyword(s) Experimental evaluation
Incremental algorithm
Recommender system
Singular value decomposition
Summary Due to the serious information overload problem on the Internet, recommender systems have emerged as an important tool for recommending more useful information to users by providing personalized services for individual users. However, in the “big data“ era, recommender systems face significant challenges, such as how to process massive data efficiently and accurately. In this paper we propose an incremental algorithm based on singular value decomposition (SVD) with good scalability, which combines the Incremental SVD algorithm with the Approximating the Singular Value Decomposition (ApproSVD) algorithm, called the Incremental ApproSVD. Furthermore, strict error analysis demonstrates the effectiveness of the performance of our Incremental ApproSVD algorithm. We then present an empirical study to compare the prediction accuracy and running time between our Incremental ApproSVD algorithm and the Incremental SVD algorithm on the MovieLens dataset and Flixster dataset. The experimental results demonstrate that our proposed method outperforms its counterparts.
Language eng
DOI 10.1016/j.jcss.2014.11.016
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Academic Press
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