论文标题
OMBA：用于在线市场篮分析的用户引导的产品表示
OMBA: User-Guided Product Representations for Online Market Basket Analysis
论文作者
论文摘要
市场篮分析（MBA）是一项识别产品之间关联的流行技术，这对于业务决策至关重要。先前的研究通常采用常规的频繁项目集挖掘算法来执行MBA。但是，它们通常未能发现产品最粒状水平的产品之间很少发生关联。同样，它们在产品之间关联中捕获时间动态的能力有限。因此，我们提出了OMBA，这是一种用于在线市场篮分析的新型表示学习技术。 OMBA共同学习了产品和用户的表示形式，以保留产品到产品和用户对产品关联的时间动态。随后，OMBA提出了一种可扩展但有效的在线方法，以使用其表示形式生成产品的关联。我们在三个现实世界数据集上进行的广泛实验表明，OMBA的表现要优于最先进的方法高达21％，同时强调很少发生强大的关联并有效地捕获关联的时间变化。
Market Basket Analysis (MBA) is a popular technique to identify associations between products, which is crucial for business decision making. Previous studies typically adopt conventional frequent itemset mining algorithms to perform MBA. However, they generally fail to uncover rarely occurring associations among the products at their most granular level. Also, they have limited ability to capture temporal dynamics in associations between products. Hence, we propose OMBA, a novel representation learning technique for Online Market Basket Analysis. OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations. Subsequently, OMBA proposes a scalable yet effective online method to generate products' associations using their representations. Our extensive experiments on three real-world datasets show that OMBA outperforms state-of-the-art methods by as much as 21%, while emphasizing rarely occurring strong associations and effectively capturing temporal changes in associations.
