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Open Access

A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
School of Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China
School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China
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Abstract

Grid-based recommendation algorithms view users and items as abstract nodes, and the information utilised by the algorithm is hidden in the selection relationships between users and items. Although these relationships can be easily handled, much useful information is overlooked, resulting in a less accurate recommendation algorithm. The aim of this paper is to propose improvements on the standard substance diffusion algorithm, taking into account the influence of the user’s rating on the recommended item, adding a moderating factor, and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm. An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results. Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

Keywords

cloud computinglink predictionbipartite graph networkrecommendation algorithmcold start problem

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Big Data Mining and Analytics
Volume 7 Issue 2,
June 2024
Pages 357-370
DOI: 10.26599/BDMA.2023.9020029
Cite this article:
Sun Q, Shi L, Liu L, et al. A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network. Big Data Mining and Analytics, 2024, 7(2): 357-370. https://doi.org/10.26599/BDMA.2023.9020029

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Received: 15 February 2023
Revised: 09 August 2023
Accepted: 10 October 2023
Published: 22 April 2024
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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