Abstract
Apache Spark, renowned for its proficiency in processing vast datasets, efficiently handles intricate processing tasks. It disperses these tasks across numerous computing instances autonomously or in conjunction with other distributed computing tools. As the volume of data burgeons and machine learning models advance, the imperative for swift and intricate feature engineering and model training intensifies. Clusters comprising multiple compute instances exhibit a noteworthy performance surge compared to individual cases, expediting data processing. However, leveraging such cluster configurations entails substantial costs due to the amalgamation of multiple compute instances (Worker Nodes) overseen by a Controller Node.
Reference14 articles.
1. 1. J. Wen, B. Y. Chen, C. D. Wang, and Z. Tian, "PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks," Proc. - IEEE Int. Conf. Data Mining, ICDM, vol. 2021-Decem, no. Icdm, pp. 729-738, 2021, https://doi.org/10.1109/ICDM51629.2021.00084
2. 2. J. R. Bock and A. Maewal, "Adversarial Learning for Product Recommendation," Ai, vol. 1, no. 3, pp. 376-388, 2020, https://doi.org/10.3390/ai1030025
3. 3. A. Akbar, P. Agarwal, and A. J. Obaid, "Recommendation engines-neural embedding to graph-based: Techniques and evaluations," Int. J. Nonlinear Anal. Appl, vol. 13, no. 1, pp. 2008-6822, 2022, [Online]. Available: http://dx.doi.org/10.22075/ijnaa.2022.5941
4. 4. G. Zhu, J. Cao, C. Li, and Z. Wu, "A recommendation engine for travel products based on topic sequential patterns," Multimed. Tools Appl., vol. 76, no. 16, pp. 17595-17612, 2017, https://doi.org/10.1007/s11042-017-4406-6
5. 5. Q. Wang, Q. V. H. Nguyen, H. Yin, Z. Huang, H. Wang, and L. Cui, "Enhancing collaborative filtering with generative augmentation," Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 548-556, 2019, https://doi.org/10.1145/3292500.3330873