Personalized brand recommendation model based on knowledge graph

Liang Guo; Xuesong Pu; Jiaxian Zhou; Jin Xiao

doi:10.1117/12.2684605

19 October 2023 Personalized brand recommendation model based on knowledge graph

Liang Guo, Xuesong Pu, Jiaxian Zhou, Jin Xiao

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270910 (2023) https://doi.org/10.1117/12.2684605
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

New retail is now a new retail model based on the new technology of the Internet era, online and offline and logistics integration. With the change of traditional retail model and user consumption habits and thus the core of the consumer, the new retail to provide quality services to consumers deeply into all walks of life. Due to the special nature of tobacco products, the traditional retail brand promotion channels are relatively limited, and the method of on-site promotion by brand managers with the help of retailer stores no longer meets the requirements. Under the new retail model, tobacco offline retail terminals can use big data analysis and recommendation technology to intelligently recommend cigarettes and personalized cigarette brands with similar styles and tastes to users based on the characteristics of new retailing, to guide customer demand and improve user experience; thus targeting marketing, reducing marketing costs and improving revenue. We propose the HLKAR model, an end-to-end recommendation model that integrates knowledge graph and attention mechanism with hybrid link prediction, to realize personalized cigarette recommendation for users in offline tobacco retail terminals.

Citation Download Citation

Liang Guo, Xuesong Pu, Jiaxian Zhou, and Jin Xiao "Personalized brand recommendation model based on knowledge graph", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270910 (19 October 2023); https://doi.org/10.1117/12.2684605

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