Improving personalized recommendations system using graph attention networks driven by perceived complexity and innovation

This paper presents a product recommendation system (GAT-RS) based on perceived complexity and perceived innovation in product reviews. Perceived complexity refers to the usability of a product, while perceived innovation refers to the extent to which something looks novel. Such aspects significantly impact consumer dynamics and business performance indicators, including user engagement and sales results. In this study, product reviews are manually annotated, and then Explainable AI (XAI) is used to improve the decision-making process of the proposed GAT-RS model. The proposed model used pre-trained SimCSE embeddings to find high-quality textual representations of product reviews. It also used Graph Attention Networks (GAT) to discover the associations between the attributes of products and the perceptions of customers about complexity and innovation. The SMOTE oversampling on classes and loss class weights functions are used to handle the imbalance between reviews during training. The evaluation of the proposed GAT-RS is done on accuracy, precision, recall, F1 score, ROC AUC, and the system was found to have a higher accuracy of 94.61% and a ROC AUC of 98.94% compared to the baseline approaches. A combination of complexity and innovation will enhance user satisfaction by aligning recommendations with preferred styles of cognition and novelty. The offered solution would also strengthen the accuracy of personalized recommendations based on customer interests.