Abstract
With the continuous development of computers and big data technology, more recommendation systems are applied in the fields of online music, online movies, games, online shopping, and so on, to solve information redundancy and effectively to recommend interesting products for users. In this paper, we implement and accelerate the Alternating-Least-Squares with Weighted-\(\lambda \)-Regularization (ALS-WR) by adopting a two-level parallel strategies on the x86-64 Zen-based CPUs. As one of the most widely used recommendation algorithms, the ALS-WR algorithm is based on matrix factorization. In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a dimensionality reduction technique that factorizes a matrix into a product of matrices. Therefore, vector and matrix operations are the computational core of the ALS-WR algorithm, accelerating these computational kernels can effectively improve the overall performance of the ALS-WR algorithm. The experimental results show that our high-performance ALS-WR implementation can achieve 185.09 s (with 100 features and 30 iterations) on the MovieLens 20 M dataset.
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Chen, M., Chen, T., Chen, Q. (2020). An Efficient Implementation of the ALS-WR Algorithm on x86 CPUs. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_12
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