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Hybrid recommendation algorithm of cross-border e-commerce items based on artificial intelligence and multiview collaborative fusion

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Abstract

E-commerce platforms apply recommendation technology to a wide variety of commercial websites to help users quickly find the product they need from a large number of products. At present, recommendation systems have been widely used in large-scale e-commerce websites, and most e-commerce shopping websites attract customers through the image information of goods. In this paper, the research status of content-based image retrieval algorithms is analysed, and how to use image content information to recommend goods is studied. A hierarchical commodity classification and retrieval system are designed. A class decision layer is used to determine the category of commodity images and then precisely retrieve the corresponding category of commodity image features. The corresponding relationship between different commodity visual features is used to recommend commodities, and a matching recommendation algorithm for commodities is proposed. Experiments show that the proposed image content-based recommendation method can coordinate with features, and its recommendation results have high accuracy in practical applications.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (71661013), Science and Technology Research Project of Jiangxi Provincial Education Department (150320), Youth Fund Project of Humanities and Social Sciences in Colleges and universities of Jiangxi Province (GL19223); "Innovation Research on Cross-border E-commerce Shopping Guide Platform Based on Big Data and AI Technology", Funded by Ministry of Education Humanities and Social Sciences Research and Planning Fund (18YJAZH042); Key Research Platform Project of Guangdong Education Department (2017GWTSCX064); The 13th Five-Year Plan Project of Philosophy and Social Science Development in Guangzhou (2018GZGJ208).

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Authors and Affiliations

  1. School of Software, Jiangxi Normal University, Nanchang, 330022, Jiangxi, China

    Bing Li

  2. School of Information Engineering, Guangzhou Vocational and Technical University of Science and Technology, Guangzhou, 510550, Guangdong, China

    Jiahua Li

  3. Research Centre for Management Science and Engineering, Jiangxi Normal University, Nanchang, 330022, Jiangxi, China

    Bing Li

  4. Scientific Research Office, Jilin Engineering Normal University, Changchun, Jilin, 130052, China

    Xijun Ou

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  1. Bing Li
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  2. Jiahua Li
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Correspondence to Xijun Ou.

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Li, B., Li, J. & Ou, X. Hybrid recommendation algorithm of cross-border e-commerce items based on artificial intelligence and multiview collaborative fusion. Neural Comput & Applic 34, 6753–6762 (2022). https://doi.org/10.1007/s00521-021-06249-3

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  • DOI: https://doi.org/10.1007/s00521-021-06249-3

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