Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review

Abstract E-commerce platforms are a primary place for people to find, compare, and ultimately purchase products. They employ Machine Learning (ML), Business Intelligence (BI), mathematical formalism, and artificial intelligence (AI) to generate valuable knowledge about customer behavior, bringing benefits for both customers themselves and sellers. The state-of-the-art in this area does not include a comprehensive and up-to-date survey that explores the most common goals of e-commerce-related studies and the suitable ML techniques and frameworks for particular cases. In this context, we introduce a systematic literature review that revisits recent initiatives to employ ML techniques on different e-commerce scenarios. The contributions to the state-of-the-art are twofold: (i) a comprehensive review of ML methods and their relationship with the target goals of e-commerce platforms, including impact on profit growth; (ii) a novel taxonomy to reorganize ML-based e-commerce initiatives, which helps researchers to compare and classify efforts in this evolving area. This comprehensive literature review enables researchers and e-commerce administrators to conduct innovation projects better and redirect budget and human resource efforts.