References
- M. Kukar-Kinney et al., “A Model of Online Shopping Cart Abandonment: Evidence from E-Tail Clickstream Data,” Journal of the Academy of Marketing Science, vol. 50, no. 5, 2022, pp. 961–980.
- S. Bandyopadhyay, S.S. Thakur, and J.K. Mandal, “Product Recommendation for E-Commerce Business by Applying Principal Component Analysis (PCA) and K-means Clustering: Benefit for the Society,” Innovations in Systems and Software Engineering, vol. 17, no. 1, 2021, pp. 45–52.
- I.W.R. Wijaya, “Development of Conceptual Model to Increase Customer Interest using Recommendation System in E-Commerce,” Procedia Computer Science, vol. 197, 2022, pp. 727–733.
- C. Wang, “Efficient Customer Segmentation in Digital Marketing using Deep Learning with Swarm Intelligence Approach,” Information Processing & Management, vol. 59, no. 6, 2022, p. 103085.
- F. Cui, H. Hu, and Y. Xie, “An Intelligent Optimization Method of E-Commerce Product Marketing,” Neural Computing and Applications, vol. 33, 2021, pp. 4097–4110.
- A. Griva et al., “A Two-Stage Business Analytics Approach to Perform Behavioural and Geographic Customer Segmentation using E-Commerce Delivery Data,” Journal of Decision Systems, vol. 33, no. 1, 2024, pp. 1–29.
- J. Oliveira et al., “Footwear Segmentation and Recommendation Supported by Deep Learning: an Exploratory Proposal,” Procedia Computer Science, vol. 219, 2023, pp. 724–735.
- M. Nasir C.I. Ezeife, “Semantic Enhanced Markov Model for Sequential E-Commerce Product Recommendation,” International Journal of Data Science and Analytics, vol. 15, no. 1, 2023, pp. 67–91.
- M. Nilashi et al., “Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques,” Arabian Journal for Science and Engineering, vol. 46, no. 9, 2021, pp. 8697–8709.
- J. Ma, X. Guo, and X. Zhao, “Identifying Purchase Intention through Deep Learning: Analyzing the Q&D Text of an E-Commerce Platform,” Annals of Operations Research, 2022, pp. 1–20.
- M.N. Mohammad et al., “Implementation of Online and Offline Product Selection System using FCNN Deep Learning: Product Analysis,” Materials Today: Proceedings, vol. 45, 2021, pp. 2171–2178.
- S.P. Nguyen, “Deep Customer Segmentation with Applications to a Vietnamese Supermarkets’ data,” Soft Computing, vol. 25, no. 12, 2021, pp. 7785–7793.
- R. Esmeli, M. Bader-El-Den, and H. Abdullahi, “An Analyses of the Effect of using Contextual and Loyalty Features on Early Purchase Prediction of Shoppers in E-Commerce Domain,” Journal of Business Research, vol. 147, 2022, pp. 420–434.
- B. Li, J. Li, and X. Ou, “Hybrid Recommendation Algorithm Of Cross-Border E-Commerce Items Based On Artificial Intelligence And Multiview Collaborative Fusion,” Neural Computing and Applications, vol. 34, no. 9, 2022, pp. 6753–6762.
- M. Nasir, C.I. Ezeife, and A. Gidado, “Improving E-Commerce Product Recommendation using Semantic Context and Sequential Historical Purchases,” Social Network Analysis and Mining, vol. 11, no. 1, 2021, p. 82.
- M.R.I. Rifat et al., “An End-to-end Machine Learning System for Mitigating Checkout Abandonment in E-Commerce,” In 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), IEEE, 2022, pp. 129–132.
- T. Kaya and C. Kaleli, “A Novel Top-N Recommendation Method for Multi-Criteria Collaborative Filtering,” Expert Systems with Applications, vol. 198, 2022, p. 116695.
- B. Kumar et al., “E-Commerce Website Usability Analysis using the Associaection Rule Mining and Machine Learning Algorithm,” Mathematics, vol. 11, no. 1, 2022, p. 25.
- A. Khan, S. Rezaei, and N. Valaei, “Social Commerce Advertising Avoidance and Shopping Cart Abandonment: Afs/QCA Analysis of German Consumers,” Journal of Retailing and Consumer Services, vol. 67, 2022, p. 102976.
- S. Wang et al., “Thanks COVID-19, I’ll Reconsider my Purchase: Can Fear Appeal Reduce Online Shopping Cart Abandonment?” Journal of Retailing and Consumer Services, vol. 64, 2022, p. 102843.
- N. Chawla and B. Kumar, “E-commerce and consumer protection in India: the emerging trend,” Journal of Business Ethics, vol. 180, no. 2, 2022, pp. 581–604.
- M. Kechinov, “Ecommerce Behavior Data from Multi Category Store,” Nov. 2019:
- P. Shao et al., “FairCF: Fairness-Aware Collaborative Filtering,” Science China Information Sciences, vol. 65, no. 12, 2022, p. 222102.
- M. Kaushal and Q.D. Lohani, “Generalized Intuitionistic Fuzzy C-Means Clustering Algorithm using an Adaptive Intuitionistic Fuzzification Technique,” Granular Computing, vol. 7, 2022, pp. 183–195.
- F. Zhu et al., “A Double Transformer Residual Super-Resolution Network for Cross-Resolution Person Re-Identification,” The Egyptian Journal of Remote Sensing and Space Science, vol. 26, no. 3, 2023, pp. 768–776.
- Y. Zhang et al., “A Curve Approximation Approach using Bio-Inspired Polar Coordinate Bald Eagle Search Algorithm,” International Journal of Computational Intelligence Systems, vol. 15, no. 1, 2022, p. 30.