An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems

@article{Luo2015AnES,
  title={An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems},
  author={Xin Luo and Mengchu Zhou and Shuai Li and Yunni Xia and Zhu-Hong You and Qingsheng Zhu and Hareton K. N. Leung},
  journal={IEEE Transactions on Industrial Informatics},
  year={2015},
  volume={11},
  pages={946-956}
}
Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work… CONTINUE READING
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