Abstract: In traditional archive management systems, keyword-based retrieval systems often fail to meet users' personalized and precise retrieval needs. To solve this problem, a knowledge graph is first constructed using bidirectional long short-term memory networks and conditional random fields and combined with user understanding-based semantic retrieval to obtain an improved personalized retrieval system. The research results show that the improved personalized retrieval system has significantly better retrieval accuracy and recall rate than traditional retrieval systems. The improved personalized retrieval system has retrieval accuracy rates of 90.24%, 89.65%, 87.52%, 96.33%, and 95.18% for students, civil servants, demobilized soldiers, law enforcement personnel, and retirees, respectively, and recall rates of 89.35%, 91.57%, 89.34%, 97.54%, and 96.63%, respectively. Applying it to the smart archive management system, the accuracy of archive retrieval, personalized recommendation accuracy, response time, and user satisfaction are significantly better than conventional management systems. The improvement and introduction of personalized retrieval systems based on user understanding and knowledge graphs have achieved significant results.

Chao Yan, “Application and Effectiveness of Improving Retrieval Systems Based on User Understanding in Smart Archive Management Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150914

@article{Yan2024,
title = {Application and Effectiveness of Improving Retrieval Systems Based on User Understanding in Smart Archive Management Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150914},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150914},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Chao Yan}
}