At present, some network personalized information recommendation systems have the problem of high CPU usage, so this paper designs the network personalized information recommendation system based on big data. The network personalized information tags are obtained, and the keywords are selected to annotate the recommended resources. Based on big data, the potential factors of user behavior are extracted and the data association features are identified. Under different recommended strategy, the retention rate and click-through rate of each traffic group ID are counted, in order to improve the traffic allocation function of the recommendation system. Test results: the average CPU utilization rate of the network personalized information recommendation system in this paper is 40.43%, which is 7.02% and 7.26% lower than the other two network personalized information recommendation systems, respectively. This shows that the designed network personalized information recommendation system has better compression resistance after combining big data technology.

Hu Xiaoqin, Pan Jinfeng. Design of Network Personalized Information Recommendation System Based on Big Data. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 14: 40-43. https://doi.org/10.25236/AJCIS.2022.051406.

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