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Volume 8 Issue 2
Feb.  2021

IEEE/CAA Journal of Automatica Sinica

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Xin Luo, Wen Qin, Ani Dong, Khaled Sedraoui and MengChu Zhou, "Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402-411, Feb. 2021. doi: 10.1109/JAS.2020.1003396
Citation: Xin Luo, Wen Qin, Ani Dong, Khaled Sedraoui and MengChu Zhou, "Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402-411, Feb. 2021. doi: 10.1109/JAS.2020.1003396

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

doi: 10.1109/JAS.2020.1003396
Funds:  This work was supported in part by the National Natural Science Foundation of China (61772493), the Deanship of Scientific Research (DSR) at King Abdulaziz University (RG-48-135-40), Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019), and the Natural Science Foundation of Chongqing (cstc2019jcyjjqX0013)
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  • A recommender system (RS) relying on latent factor analysis usually adopts stochastic gradient descent (SGD) as its learning algorithm. However, owing to its serial mechanism, an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems. Aiming at addressing this issue, this study proposes a momentum-incorporated parallel stochastic gradient descent (MPSGD) algorithm, whose main idea is two-fold: a) implementing parallelization via a novel data-splitting strategy, and b) accelerating convergence rate by integrating momentum effects into its training process. With it, an MPSGD-based latent factor (MLF) model is achieved, which is capable of performing efficient and high-quality recommendations. Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm, an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

     

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    Highlights

    • Proposing an MPSGD algorithm with faster convergence than existing parallel SGD algorithms when building an LF model for an RS.
    • Performing theoretical analysis and algorithm design for the proposed MPSGD-based LF model.
    • Conducting empirical studies on four HiDS matrices generated by industrial applications to evaluate the proposed model.

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