Title: The design of library resource personalised recommendation system based on deep belief network

Authors: Min Fu

Addresses: Jiangsu Vocational Institute of Commerce Library, Nanjing 211168, China

Abstract: Aiming at the problems of low accuracy and user satisfaction of traditional library resource recommendation system, this paper designs a library resource personalised recommendation system based on deep belief network. Firstly, the architecture of library resource personalised recommendation system is designed and real-time recommendation module is added. For the real-time recommendation module, the probability function of the best resource in the deep belief network is obtained by combining several limited Boltzmann machines and maximum likelihood principles. The bias value and weight of the visible layer and the hidden layer of the deep belief network are corrected by the contrast divergence method to make the recommendation result more accurate. The experimental results show that the recommendation accuracy of the system can reach more than 97%, the recall rate can reach 92.5%, and the user satisfaction is more than 94.8%, indicating that the system has effectively improved the recommendation effect.

Keywords: deep belief network; limit Boltzmann machine; parameter modification; library resources; personalised recommendation.

DOI: 10.1504/IJASS.2023.134370

International Journal of Applied Systemic Studies, 2023 Vol.10 No.3, pp.205 - 219

Received: 15 Mar 2022
Accepted: 05 Jul 2022
Published online: 19 Oct 2023 *

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