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Market Basket Analysis (MBA) is a popular technique to identify associationsbetween products, which is crucial for business decision making. Previousstudies typically adopt conventional frequent itemset mining algorithms toperform MBA. However, they generally fail to uncover rarely occurringassociations among the products at their most granular level. Also, they havelimited ability to capture temporal dynamics in associations between products.Hence, we propose OMBA, a novel representation learning technique for OnlineMarket Basket Analysis. OMBA jointly learns representations for products andusers such that they preserve the temporal dynamics of product-to-product anduser-to-product associations. Subsequently, OMBA proposes a scalable yeteffective online method to generate products' associations using theirrepresentations. Our extensive experiments on three real-world datasets showthat OMBA outperforms state-of-the-art methods by as much as 21%, whileemphasizing rarely occurring strong associations and effectively capturingtemporal changes in associations.

OMBA: User-Guided Product Representations for Online Market Basket Analysis