Research on a cigarette placement method based on multi-source data and neural networks

Taicheng Wei; Shipeng Hou; Haoran Zhu; Haiying Li; Qiaoge Zhang

doi:10.1117/12.2673735

22 May 2023 Research on a cigarette placement method based on multi-source data and neural networks

Taicheng Wei, Shipeng Hou, Haoran Zhu, Haiying Li, Qiaoge Zhang

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401M (2023) https://doi.org/10.1117/12.2673735
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

Product placement is a key part of the whole tobacco industry chain. Traditional placement often costs a large manual workload and is strongly influenced by personal experience. To achieve accurate cigarette placement, we propose a set of data-driven intelligent strategies in different segmented markets. An accurate retailer classification cigarette placement algorithm based on the attributes of business circles is proposed, business circle data outside the tobacco database is introduced, and a neural network cigarette placement algorithm with the fusion of business circle features is established. At last, the experimental results show that the average accuracy of the brand (Zhenlonglingyun) based on feature fusion is 84.7% and the brand (Nanjingyinghong) accounts for 90.1%. Through data cleaning and feature fusion, the deep learning model can be trained to generate customized marketing strategies and achieve intelligent and accurate cigarette placement.

Citation Download Citation

Taicheng Wei, Shipeng Hou, Haoran Zhu, Haiying Li, and Qiaoge Zhang "Research on a cigarette placement method based on multi-source data and neural networks", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401M (22 May 2023); https://doi.org/10.1117/12.2673735

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