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 Using Stochastic Gradient Descent On Parallel Recommender System with Stream Data
Tác giả hoặc Nhóm tác giả: Nguyen, Si Thin
Van, Hung Trong
Vo, Ngoc Dat
Ngo, Le Quan
Nơi đăng: 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD);; Số: 7;Từ->đến trang: 978-1-6654-6582-3 (e) 978-1-6654-6583-0 (p);Năm: 2022
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Stochastic gradient descent (SGD) and Alternating least squares (ALS) are two popular algorithms applied on matrix factorization. Moreover recent researches pay attention to how to parallelize them on daily increading data. About large-scale datasets issue, however, SGD still suffers with low convergence by depending on the parameters. While ALS is not scalable due to the cubic complexity with the target time rank. The remaining issue, how to operate system, almost parallel algorithms conduct matrix factorization on a batch of training data while the system data is real-time. In this work, the authors proposed FSGD algorithm overcomes drawbacks in large-scale issue base on coordinate descent, a novel optimization approach. According to that, algorithm updates rank-one factors one by one to get faster and more stable convergence than SGD and ALS. In addition, FSGD is feasible to paralleize and operates on a stream of incoming data. The experimental results show that FSGD performs much better in solving the matrix factorization issue compared to existing state-of-the-art parallel models.
ABSTRACT
Stochastic gradient descent (SGD) and Alternating least squares (ALS) are two popular algorithms applied on matrix factorization. Moreover recent researches pay attention to how to parallelize them on daily increading data. About large-scale datasets issue, however, SGD still suffers with low convergence by depending on the parameters. While ALS is not scalable due to the cubic complexity with the target time rank. The remaining issue, how to operate system, almost parallel algorithms conduct matrix factorization on a batch of training data while the system data is real-time. In this work, the authors proposed FSGD algorithm overcomes drawbacks in large-scale issue base on coordinate descent, a novel optimization approach. According to that, algorithm updates rank-one factors one by one to get faster and more stable convergence than SGD and ALS. In addition, FSGD is feasible to paralleize and operates on a stream of incoming data. The experimental results show that FSGD performs much better in solving the matrix factorization issue compared to existing state-of-the-art parallel models.
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