Article: Hybrid collaborative recommendation of cross-border e-commerce products based on multidimensional evaluation Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.1 pp.102 - 116 Abstract: With the rapid development of globalisation and e-commerce, cross-border e-commerce platforms are facing the challenge of improving user experience and recommendation system efficiency while meeting the diverse needs of consumers. Traditional recommendation systems rely heavily on users' historical behaviour and simple rating data. However, these methods often face problems such as data sparsity and single recommendation results in practical applications. Therefore, this article proposes a hybrid collaborative recommendation method for cross-border e-commerce products based on multidimensional evaluation, which fully utilises users' multidimensional evaluation information of products to address the complexity of cross-border e-commerce. Then, the system framework and algorithm flow were presented. Finally, the improved algorithm proposed in this paper was experimentally analysed using a cross-border e-commerce enterprise order dataset. Compared with traditional collaborative filtering recommendation algorithms, it reduced the impact of data sparsity in collaborative filtering recommendation algorithms and verified that the improved algorithm has better recommendation performance. Inderscience Publishers - linking academia, business and industry through research

Title: Hybrid collaborative recommendation of cross-border e-commerce products based on multidimensional evaluation

Authors: Liyun Pan

Addresses: Business and Tourism Institute, Hangzhou Vocational and Technical College, Hangzhou, 310018, China

Abstract: With the rapid development of globalisation and e-commerce, cross-border e-commerce platforms are facing the challenge of improving user experience and recommendation system efficiency while meeting the diverse needs of consumers. Traditional recommendation systems rely heavily on users' historical behaviour and simple rating data. However, these methods often face problems such as data sparsity and single recommendation results in practical applications. Therefore, this article proposes a hybrid collaborative recommendation method for cross-border e-commerce products based on multidimensional evaluation, which fully utilises users' multidimensional evaluation information of products to address the complexity of cross-border e-commerce. Then, the system framework and algorithm flow were presented. Finally, the improved algorithm proposed in this paper was experimentally analysed using a cross-border e-commerce enterprise order dataset. Compared with traditional collaborative filtering recommendation algorithms, it reduced the impact of data sparsity in collaborative filtering recommendation algorithms and verified that the improved algorithm has better recommendation performance.

Keywords: recommendation system; cross border e-commerce; collaborative filtering; multidimensional evaluation.

DOI: 10.1504/IJICT.2025.144012

International Journal of Information and Communication Technology, 2025 Vol.26 No.1, pp.102 - 116

Received: 27 Oct 2024
Accepted: 25 Nov 2024
Published online: 20 Jan 2025 *

Title: Hybrid collaborative recommendation of cross-border e-commerce products based on multidimensional evaluation

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