Title: Construction of an intelligent recommendation model for microblog short videos based on ABC-BPNN

Authors: Yunpeng Gu

Addresses: Art Design College, Yangzhou Polytechnic Institute, Yangzhou, 225107, China

Abstract: To improve the intelligent recommendation function of the Weibo short video to achieve more accurate recommendation services, this experiment proposes a combination of a back propagation neural network (BPNN) and the artificial bee colony (ABC) algorithm intelligent recommendation model, namely ABC-BPNN. The ABC algorithm mainly realises global optimisation by simulating the biological process of bee colonies looking for honey sources. In addition, given the problem of huge data and complex sample space in the video recommendation system, the experiment chooses to utilise principal component analysis to diminish the dimensionality of the sample space of massive data to reduce the complexity of the overall recommendation algorithm. The outcomes demonstrate that the video recommendation accuracy of the ABC-BPNN model is increased by 12.15%, 8.67%, and 5.21% compared with the BP model, genetic algorithm-back propagation neural network (GA-BP), and particle swarm optimisation-back propagation neural network (PSO-BP) respectively. Therefore, the model constructed in this experiment can optimise the limitations of traditional recommendation models and achieve personalised services for user recommendations, which have good practicality.

Keywords: back propagation neural network; BPNN; bee colony intelligence algorithm; short video; principal component analysis; intelligent recommendation.

DOI: 10.1504/IJCSYSE.2024.142768

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.264 - 272

Received: 20 Mar 2023
Accepted: 06 Jun 2023
Published online: 21 Nov 2024 *

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