With the advent of the era of big data, libraries are faced with the need to make more use of user characteristic data to provide personalized services. However, due to the difficulty of acquiring real user characteristics data, it is difficult to carry out effective analysis and application. This paper proposes a genetic algorithm-based approach to generate simulated user feature data to enhance library service quality. Through genetic algorithms, we can simulate users' borrowing behavior, search behavior and participation behavior. The experimental results show that the generated simulated data is basically consistent with the real data in the distribution of behavior characteristics, which can provide accurate user characteristics data for the library, so as to better meet the needs of users and provide personalized services.

Chu Chenhui, Li Guohui. Library User Feature Data Generation Based on Genetic Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 1: 35-41. https://doi.org/10.25236/AJCIS.2024.070106.

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