Author(s)

Qingyi Lu ¹, Xinyu Guo ², Haowei Yang ³, Zhizhong Wu ⁴, Chunyan Mao ⁵

Affiliation(s)

¹ Department of Computer Science, Brown University, Providence, RI, USA
² Computer Science, Tandon School of Engineering, Brooklyn, NY, USA
³ Cullen College of Engineering, University of Houston, Houston, TX, USA
⁴ College of Engineering, UC Berkeley, Berkeley, CA, USA
⁵ School of Information and Communication Engineering, Shanghai Jiao Tong University, Shanghai, China

Corresponding Author

Qingyi Lu

ABSTRACT

With the rapid development of big data technology, recommendation systems have been widely applied in various fields. However, traditional recommendation systems face performance bottlenecks and inefficiencies when processing massive amounts of data. This paper proposes an adaptive algorithm recommendation system based on a parallel data mining platform. By integrating parallel computing technology with adaptive algorithms, the system enhances processing capabilities and recommendation effectiveness. This paper first introduces the relevant theoretical and technical background, then designs the overall system architecture and key modules, and provides a detailed description of the selection and implementation process of the adaptive algorithm. Experimental validation shows significant improvements in recommendation accuracy and processing efficiency. Finally, the paper presents practical application case studies demonstrating the system's utility and offers insights into future research directions.

KEYWORDS

Parallel data mining, recommendation system, adaptive algorithm, parallel computing, system architecture, performance optimization

CITE THIS PAPER

Qingyi Lu, Xinyu Guo, Haowei Yang, Zhizhong Wu, Chunyan Mao, Research on Adaptive Algorithm Recommendation System Based on Parallel Data Mining Platform. Advances in Computer, Signals and Systems (2024) Vol. 8: 23-33. DOI: http://dx.doi.org/10.23977/acss.2024.080503.

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