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

• Published in: IEEE transactions on industrial informatics Vol. 10; no. 2; pp. 1273 - 1284
• Main Authors: Luo, Xin, Zhou, Mengchu, Xia, Yunni, Zhu, Qingsheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TII.2014.2308433

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.