Data-driven online learning recommendation system

Yuxiang Liu; Han Wang; Minghao Liu

doi:10.1117/12.3025590

8 April 2024 Data-driven online learning recommendation system

Yuxiang Liu, Han Wang, Minghao Liu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309044 (2024) https://doi.org/10.1117/12.3025590
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The purpose of this study is to improve the performance and efficiency of online learning recommendation systems. The performance and efficiency of recommendation systems depend on multiple factors, including distributed computing, data storage, collaborative filtering algorithms, and optimization of recommendation accuracy. To address these issues, this study built a distributed computing system and HDFS data storage on the Spark platform, and implemented multiple recommendation algorithms to improve the performance and efficiency of the recommendation system. Through analysis of the Mooper dataset, this study conducted feature modeling and proposed a learning resource recommendation system based on Java, Scala, and web technologies, which can provide users with a better learning resource recommendation experience. In addition, this study also developed an anomaly learning behavior detection algorithm, which can detect anomalies in user behavior. Experimental results show that the accuracy and recall rate of the recommendation system have been improved, and the anomaly detection algorithm has high accuracy and robustness, thereby improving the reliability and stability of the recommendation system. In summary, the innovation of this study lies in proposing a recommendation system based on distributed computing and collaborative filtering algorithm, and making new progress in anomaly learning behavior detection. This study provides a new method for the field of recommendation systems, which can provide better solutions for the performance and efficiency of recommendation systems.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuxiang Liu, Han Wang, and Minghao Liu "Data-driven online learning recommendation system", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309044 (8 April 2024); https://doi.org/10.1117/12.3025590

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KEYWORDS

Online learning

Matrices

Distributed computing

Computing systems

Tunable filters

Data modeling

Data processing

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