Purchase prediction has an important role for decision-makers in e-commerce to improve consumer experience, provide personalised recommendations and increase revenue. Many works investigated purchase prediction for session logs by analysing users’ behaviour to predict purchase intention after a session has ended. In most cases, e-shoppers prefer to be anonymous while browsing the websites and after a session has ended, identifying users and offering discounts can be challenging. Therefore, after a session ends, predicting purchase intention may not be useful for the e-commerce strategists. In this work, we propose and develop an early purchase prediction framework using advanced machine learning models to investigate how early purchase intention in an ongoing session can be predicted. Since users could be anonymous, this could help to give real-time offers and discounts before the session ends. We use dynamically created session features after each interaction in a session, and propose a utility scoring method to evaluate how early machine learning models can predict the probability of purchase intention. The proposed framework is validated with a real-world dataset. Computational experiments show machine learning models can identify purchase intention early with good performance in terms of Area Under Curve (AUC) score which shows success rate of machine learning models on early purchase prediction.

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.

Spoilage in processed meat products, such as poultry and pork sausages, presents significant challenges for food safety, quality control, and waste reduction. This study presents a machine learning-based framework to classify spoilage intensity levels using sensory, physicochemical, and microbiological features. To overcome limitations caused by small datasets, we applied synthetic data augmentation using a tabular variational autoencoder (TVAE) to generate high-fidelity samples that enhance model generalization. Additionally, traditional oversampling techniques such as SMOTE and ADASYN were employed for comparative purposes and to further address class imbalance issues. Seven machine learning classifiers were evaluated logistic regression, support vector machine, K -nearest neighbors, random forest, gradient boosting, voting classifier, and multilayer perceptron. The best classification performance was achieved when models were trained on GAN-based synthetic data and tested on real samples. For poultry sausage spoilage prediction, the gradient boosting classifier reached the highest accuracy of 97%. For pork sausages, random forest achieved the highest accuracy of 95%. These results confirm the effectiveness of data augmentation in improving predictive robustness. To ensure model transparency, we integrated explainable AI techniques SHAP and LIME into the pipeline. These analyses revealed that sampling time, CO $$_2$$ 2 concentration, pH, and microbial species such as Lactobacillus curvatus and Leuconostoc carnosum were among the most influential features in spoilage prediction. The combination of synthetic data generation and interpretable machine learning enables a reliable, scalable, and explainable approach to spoilage classification. This methodology has strong potential for enhancing quality control systems in the meat industry while reducing waste and improving safety along the food supply chain. Graphical

The selection of relevant variables is critical for providing personalized product and service recommendations on e-commerce businesses. However, the integration of e-loyalty-related features into recommender systems remains underexplored. This study aims to investigate the impact of incorporating e-loyalty indicators, such as purchase frequency and platform engagement, on the performance of recommender systems in the context of e-commerce businesses. Using three well-established recommender system models and four real-world datasets, we conducted computational experiments to assess performance improvements when e-loyalty features are incorporated. The results show that integrating e-loyalty-related features significantly enhances the performance of recommendation systems, with sequential deep neural networks outperforming other algorithms. Our study contributes to the literature by highlighting the value of leveraging customer loyalty data to enhance recommendation accuracy. Theoretical implications include underscoring the importance of using longitudinal user engagement data in recommender systems to move beyond static personalization toward adaptive, behavior-aware technologies. From a practical perspective, our findings suggest that incorporating e-loyalty features can improve recommendation accuracy, offering valuable insights for e-commerce businesses seeking to personalize their services. This research offers original contributions by focusing on the role of loyalty-driven features in improving recommender systems, an area that remains largely underexplored.