Author(s)

Zhen Li ¹

Affiliation(s)

¹ Library, Jiangsu Maritime Institute, Nanjing, 211100, China

Corresponding Author

Zhen Li

ABSTRACT

This article focuses on the individualized service of campus library, the aim is to improve service quality and maximize resource utilization by employing intelligent recommendation algorithms. Based on the theory of individualized service and intelligent recommendation algorithm of campus library, this article designs an individualized service system covering system architecture, functional modules and algorithm models. Taking 500 students in a university as the object, the experiment was carried out, they were split into two groups: the test group and the comparison group. Individualized service system based on intelligent recommendation algorithm and traditional library recommendation service were adopted respectively. Through rigorous experimental verification, the findings indicate that the experimental group achieved a recommendation accuracy of 76% and a recall rate of 63.33%, both of which are considerably higher than the control group’s results (45% and 38.57%). These results demonstrate that the campus library's personalized service system, powered by the intelligent recommendation algorithm, is both practical and effective. It better aligns with user needs and offers viable solutions for the development of personalized services in campus libraries.

KEYWORDS

Intelligent Recommendation Algorithm; Campus Library; Individualized Service

CITE THIS PAPER

Zhen Li, Individuation of campus library service based on intelligent recommendation algorithm. Information and Knowledge Management (2025) Vol. 6: 112-117. DOI: http://dx.doi.org/10.23977/infkm.2025.060115.

REFERENCES

[1] Chen S, Huang L, Lei Z, et al. Research on personalized recommendation hybrid algorithm for interactive experience equipment[J]. Computational Intelligence, 2020, 36(3):1348-1373.
[2] Abinaya S, Alphonse A S, Kavithadevi S A K. Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder[J]. Neural processing letters, 2023, 55(5):6843-6864.
[3] Jin B, Liu D, Li L. Research on social recommendation algorithm based on fuzzy subjective trust[J]. Connection Science, 2022, 34(1):1540-1555.
[4] Liu Y, You X, Liu S. Multi-ant colony optimization algorithm based on hybrid recommendation mechanism[J]. Applied Intelligence, 2022, 52(8):8386-8411.
[5] Guo D, Wang C. Sequence recommendation based on deep learning[J]. Computational Intelligence, 2020, 36(4): 1704-1722.
[6] Liu Y, Pei A, Wang F, et al. An attention‐based category‐aware GRU model for the next POI recommendation[J]. International Journal of Intelligent Systems, 2021, 36(7): 3174-3189.
[7] Yuan Husheng, Tang Jiale, Zhao Xichen, et al. ChatLib: Reconstructing the Knowledge Service Platform of Smart Libraries [J]. Journal of Academic Libraries, 2024, 42(2): 72-80.
[8] Cai Yingchun. Re-creating Library Service Scenarios from the Perspective of Spatial Narrative [J]. Library and Information Service, 2023, 67(21): 48-55.
[9] Liu Baisong, Yang Chunyan, Yin Wenting, et al. Modernization of Library Services Driven by Intelligent Technologies: Transformation and Innovation [J]. Journal of Academic Libraries, 2024, 42(4): 13-19.
[10] Chu Jiewang, Du Xiuxiu, Li Jiaxuan. The Impact of AI-generated Content on Smart Library Services and Its Application Prospects [J]. Information Studies: Theory & Application, 2023, 46(5): 6-13.

Sponsors, Associates, and Links

---

• Journal of Language Testing & Assessment
  
  
• Military and Armament Science
  
  
• Media and Communication Research
  
  
• Journal of Human Movement Science
  
  
• Art and Performance Letters
  
  
• Lecture Notes on History
  
  
• Lecture Notes on Language and Literature
  
  
• Philosophy Journal
  
  
• Science of Law Journal
  
  
• Journal of Political Science Research
  
  
• Journal of Sociology and Ethnology
  
  
• Advances in Broadcasting