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Decoding the digital market: understanding how machine learning shapes e-commerce

Abiy Abinet Mamo et al.

International Journal of Electronic Business2026https://doi.org/10.1504/ijeb.2026.151462article
AJG 1
Weight
0.50

What the paper says

Machine learning (ML) has intricately woven itself into the fabric of e-commerce, exerting a profound influence on its evolutionary path. This research paper conducts a thorough exploration of the dynamic intersection between ML and e-commerce, offering a detailed examination of various ML algorithms applied in this domain. Through illuminating case studies, the practical applications of these algorithms are elucidated. The paper also scrutinises the contemporary landscape, dissecting the challenges faced in implementing ML within e-commerce settings. In addition, it outlines future directions and emerging trends, presenting a forward-looking perspective that enriches the ongoing discourse on the symbiotic relationship between ML and the continually evolving landscape of e-commerce.

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https://doi.org/https://doi.org/10.1504/ijeb.2026.151462

Or copy a formatted citation

@article{abiy2026,
  title        = {{Decoding the digital market: understanding how machine learning shapes e-commerce}},
  author       = {Abiy Abinet Mamo et al.},
  journal      = {International Journal of Electronic Business},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijeb.2026.151462},
}

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Decoding the digital market: understanding how machine learning shapes e-commerce

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.