Family profile mining in retailing

摘要

In the era of personalized marketing, the ability to leverage data analysis to recommend tailored products is a key competitive advantage for retailers. Family profiles are an essential aspect of customer information to boost the performance of knowledge-based recommendations. This study integrates positive and unlabeled learning and feature selection techniques to design a novel and flexible family profiling algorithm that tags target customers based on unlabeled transaction data. The empirical evaluation shows that our algorithm outperforms other algorithms in terms of the recall rate of families of the target tag. The knowledge of inferred family profiles also enhances the product recommendation performance. Our algorithm can help retailers efficiently target customers based on family profiles for better marketing performance.