1. A Fast Deep AutoEncoder for high-dimensional and sparse matrices in recommender systems.
- Author
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Jiang, Jiajia, Li, Weiling, Dong, Ani, Gou, Quanhui, and Luo, Xin
- Subjects
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SPARSE matrices , *RECOMMENDER systems , *DATA distribution , *FACTOR analysis , *LEARNING ability , *MATRICES (Mathematics) - Abstract
A latent factor analysis (LFA)-based model has outstanding performance in extracting desired patterns from High-dimensional and Sparse (HiDS) data for building a recommender systems. However, they mostly fail in acquiring non-linear features from an HiDS matrix. An AutoEncoder (AE)-based model can address this issue efficiently, but it requires filling unknown data of an HiDS matrix with pre-assumptions that leads to the following limitations: a) prefilling unknown data of an HiDS matrix might skew its known data distribution to generate ridiculous recommendations; and b) incorporating a deep AE-style structure to improve its representative learning ability. Experimental results on three HiDS matrices from real recommender systems show that an FDAE-based model significantly outperforms state-of-the-art recommenders in terms of recommendation accuracy. Meanwhile, its computational efficiency is comparable with the most efficient recommenders with the help of parallelization. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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