OMBA: User-Guided Product Representations for Online Market Basket Analysis
| Autor: | Ling Luo, Amila Silva, Christopher Leckie, Shanika Karunasekera |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Affinity analysis 02 engineering and technology Machine learning computer.software_genre Dynamics (music) 020204 information systems Business decision mapping Scalability 0202 electrical engineering electronic engineering information engineering Online method 020201 artificial intelligence & image processing Product (category theory) Artificial intelligence business computer Transaction data Feature learning |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575 ECML/PKDD (1) |
| DOI: | 10.1007/978-3-030-67658-2_4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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