基于气味知识图谱的成品卷烟多标签预测

Multi-label prediction of cigarettes based on odor knowledge graphs

在数字经济时代,利用大数据与人工智能技术帮助卷烟零售管理实现转型升级已形成共识.标签预测在新零售模式下有效匹配卷烟产品与消费者的需求,从而促进销售.本文提出在新零售模式下,结合知识图谱与自然语言处理技术,从不同维度特征增强消费者感受野,利用气味知识图谱驱动每个气味感官与数据视图,共同学习多标签特征的贡献,保持特征内核之间的一致性和潜在表征的相似性,实现成品卷烟的多标签预测,从而提高卷烟销售效率和提升消费者体验感知.实验结果表明该方法在卷烟营销管理预测领域的有效性.

In the digital economy,there is a consensus to use big data and artificial intelligence technologies to help transform and upgrade cigarette retail management.Label prediction effectively matches cigarette products and consumers needs in the new retail model to promote sales.In this paper,we propose to combine knowledge graph and natural language processing technology to enhance consumer perception wild from different dimensional features in the new retail model,and use odor knowledge graph to drive each scent sensory and data view to jointly learn the contribution of multi-label features,maintain the consistency between feature kernels and the similarity of potential representations,and realize multi-label prediction of finished cigarettes,so as to improve cigarette sales efficiency and enhance consumer experience perception.The experimental results demonstrate the effectiveness of this approach in the field of cigarette marketing management prediction.