Development and Optimization of Social Network Systems on Machine Learning

Huijie Pan

doi:10.71222/f9btc494

Authors

Huijie Pan Identity Department, PayPal Inc., San Jose, California, 95131, United States Author

DOI:

https://doi.org/10.71222/f9btc494

Keywords:

machine learning, social networking, personalized recommendation, emotion analysis, system optimization

Abstract

The intelligent and personalized development of social network system cannot be separated from the support of machine learning technology. This paper discusses the application of machine learning in social network system, focusing on the implementation of data flow design, machine learning model integration, personalized recommendation, social graph analysis, emotion recognition and security protection. At the same time, the strategy of optimizing system throughput, computing efficiency, data transmission and resource allocation through machine learning is studied to improve system performance.

References

1. C. K. Ch'ng, “Hybrid machine learning approach for predicting E-wallet adoption among higher education students in Ma-laysia,” J. Inf. Commun. Technol., vol. 23, no. 2, pp. 177–210, 2024, doi: 10.32890/jict2024.23.2.2.

2. A. A. de Souza, et al., “249.1: Interpretable prediction of kidney allograft rejection using machine learning: A comparison be-tween linear and non-linear models,” Transplant., vol. 108, no. 9S, 2024, doi: 10.1097/01.tp.0001064836.44766.95.

3. M. B. Pasha, et al., “Tribo-informatics approach to predict wear and friction coefficient of Mg/Si3N4 composites using machine learning techniques,” Tribol. Int., vol. 196, p. 109696, 2024, doi: 10.1016/j.triboint.2024.109696.

4. Z. Huang, et al., “Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model,” Microbiol. Spectr., vol. 12, no. 5, p. e02720-23, 2024, doi: 10.1128/spectrum.02720-23.

5. K. K. Sinha and S. Suvvari, “PRMNBR: Personalized Recommendation Model for Next Basket Recommendation Using User's Long-Term Preference, Short-Term Preference, and Repetition Behaviour,” Revue d'Intell. Artif., vol. 38, no. 4, 2024, doi: 10.18280/ria.380417.