  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.
