Atıf İçin Kopyala
Eşmeli R., Gokce A.
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH, cilt.20, sa.1, ss.1-18, 2025 (SSCI)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
20
Sayı:
1
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Basım Tarihi:
2025
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Doi Numarası:
10.3390/jtaer20010028
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Dergi Adı:
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
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Derginin Tarandığı İndeksler:
Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, Metadex, Directory of Open Access Journals, DIALNET, Civil Engineering Abstracts
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Sayfa Sayıları:
ss.1-18
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Van Yüzüncü Yıl Üniversitesi Adresli:
Evet
Özet
To optimize personalized offers and reduce cart abandonment, it is essential to understand customer behavior in e-commerce after products are added to the cart. Although purchase prediction models are well researched, session-level changes, including price variations, product category shifts, and geographical context, are less examined concerning their impact on machine learning models for predicting purchase behavior after cart additions. This study incorporates these factors into machine learning models to examine their impacts on predictions using explainable AI techniques. The comprehensive experimental results obtained from two datasets and eight models demonstrate that machine learning algorithms can achieve an F1 score of 89% in predicting purchase behavior following cart additions. This study highlights the significant impact of session-specific factors, like price fluctuations, category transitions, and geographical context, coupled with consumers’ previous browsing patterns, on model predictions.