Cigarette multi-view labelling forecasting in the new retail model

Combining big data, artificial intelligence, deep learning and other technical means to allow consumer tagging to achieve accurate marketing is an intelligent new retail innovation model. To solve these problems, personalized characteristic labels for goods are established to achieve increased efficiency and reduced costs and to improve the perception of customer-consumer experience. A multi-view label prediction of personalized odour characteristics is proposed. Specifically, a gating mechanism is used to coordinate the odour knowledge graph with cigarette consumption review data. and jointly learn the weight of each data view's contribution to the multi-label features. Then, combining the prediction results of all classifiers and the learned contribution weights, a final prediction can be made. The research is experimentally validated as pioneering work in the field of odour tagging recommendation and also has good effectiveness.