An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NM...

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Vydáno v:IEEE transactions on industrial informatics Ročník 10; číslo 2; s. 1273 - 1284
Hlavní autoři: Luo, Xin, Zhou, Mengchu, Xia, Yunni, Zhu, Qingsheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.05.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Algorithm design and analysis
Algorithms
Collaborative filtering (CF)
Complexity
Computational complexity
Computational modeling
Computer vision
Filtering
Filtration
Informatics
non-negative matrix-factorization (NMF)
recommender system
Recommender systems
single-element-based approach
Sparse matrices
Tikhonov regularization
Training
ISSN:1551-3203, 1941-0050
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Shrnutí:Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2014.2308433