The widespread expansion of online retail has transformed recommender systems into an essential component of
modern e-commerce platforms by guiding customers toward products that meet with their interests and purchasing
behavior. Recent developments in machine learning have greatly enhanced the accuracy of recommendation models;
however, several practical challenges still remain, many existing solutions provide little explanation of how individual
recommendations are generated. This lack of interpretability can reduce user confidence and restrict the adoption of AIbased recommendation models in applications where transparent decision-making is required. To address this challenge,
the present study introduces an Explainable Artificial Intelligence (XAI)-based recommendation method that brings
together feature engineering, the Synthetic Minority Oversampling Technique(SMOTE), Extreme Gradient
Boosting(XGBoost), and SHapley Additive exPlanations(SHAP). The workflow begins by preprocessing user–product
interaction data and constructing informative features, including user activity, product popularity, and price buckets. The
class imbalance problem is then handled using SMOTE before training an XGBoost classifier to estimate recommendation
probabilities. To make the prediction process easier to understand, SHAP explains the contribution of each feature to
individual recommendation outcomes at both the global and local levels. Experimental evaluation demonstrates that the
proposed approach achieves strong predictive performance in terms of Accuracy, Precision, Recall, F1-score, and ROCAUC while maintaining a high level of model interpretability. By combining reliable prediction with meaningful
explanations, the proposed recommendation approach strengthens user trust and offers a practical solution for
deployment in intelligent e-commerce environments.
Shalini M. R., N. K· International Journal of Inn...· 0 citations
Sentiment analysis has become an essential Natural Language Processing (NLP) technique for extracting opinions and emotions from textual data generated through social media, online reviews, blogs, and customer feedback. Although deep learning models such as Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and other transformer-based architectures have achieved remarkable accuracy in sentiment classification, their black-box nature limits interpretability and user trust. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent and understandable explanations for model predictions, enabling users to identify the key words, phrases, and contextual features that influence sentiment classification. This paper presents a comprehensive study of Explainable AI techniques applied to sentiment analysis, focusing on both model-agnostic methods, including Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and attention-based explanation mechanisms. The proposed framework integrates text preprocessing, feature extraction using TF-IDF and contextual embeddings, sentiment classification through machine learning and deep learning models, and explanation generation to improve model transparency. Performance is evaluated using publicly available sentiment datasets based on accuracy, precision, recall, F1-score, and explanation quality. Experimental results demonstrate that XAI techniques significantly enhance the interpretability of sentiment prediction without substantially compromising classification performance. Furthermore, explainable sentiment analysis supports fairness assessment, bias detection, regulatory compliance, and informed decision-making in critical domains such as healthcare, finance, education, and social media analytics. The findings highlight that integrating explainability with sentiment analysis not only increases model reliability but also promotes greater user confidence and responsible deployment of artificial intelligence systems.
Aishwarya P. A., N. K· International Journal of Res...· 0 citations
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