Title: Design of library information recommendation system integrating transfer learning and population intelligence optimisation

Authors: Fang He

Addresses: Department of Library, Zhongnan University of Economics and Law, Wuhan, Hubei Province, China

Abstract: Traditional classification and recommendation methods encounter limitations due to high dimensionality, insufficient annotation and the heterogeneous nature of user interests. To address these challenges, this study proposes a novel framework for user interest classification and book recommendation, named Transfer Convolutional Adaptive Support Vector Machine (T-CASVM). This framework integrates deep transfer learning with Particle Swarm Optimisation (PSO). It utilises a deep transfer convolutional neural network with shared weights to extract features from both source and target domains, thereby mitigating distributional discrepancies by simultaneously optimising classification loss and domain loss. Furthermore, PSO is employed to refine the classifier, improving both accuracy and computational efficiency. The framework calculates cosine similarity between the target user and others to provide personalised book recommendations. Experimental results on the public Book-Crossing data set show that T-CASVM outperforms traditional methods, achieving over 0.79 in precision index concerning the user interest classification task.

Keywords: swarm intelligence; PSO; transfer learning; recommendation system.

DOI: 10.1504/IJCAT.2025.149865

International Journal of Computer Applications in Technology, 2025 Vol.77 No.1/2, pp.74 - 84

Received: 03 Mar 2025
Accepted: 24 May 2025
Published online: 14 Nov 2025 *

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