Unsupervised Customer Segmentation with Knowledge Graph Embeddings.
International Workshop on Multimodal Human Understanding for the Web and Social Media(2022)
摘要
We propose an unsupervised customer segmentation method from behavioral data. We model sequences of beer consumption from a publicly available dataset of 2.9M reviews of more than 110,000 brands over 12 years as a knowledge graph, learn their representations with knowledge graph embedding models, and apply off-the-shelf cluster analysis. Experiments and clusters interpretation show that we learn meaningful clusters of beer customers, without relying on expensive consumer surveys or time-consuming data annotation campaigns.
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