Enhancing Personalized Explainable Recommendations with Transformer Architecture and Feature Handling

The advancement of explainable recommendations aims to improve the quality of textual explanations for recommendations. Traditional methods primarily used Recurrent Neural Networks (RNNs) or their variants to generate personalized explanations. However, recent research has focused on leveraging Transformer architectures to enhance explanations by extracting user reviews and incorporating features from interacted items. Nevertheless, previous studies have failed to fully exploit the relationship between reviews and user ratings to generate more personalized explanations. In this paper, we propose a novel model named EPER (Enhanced Personalization for Explainable Recommendation), which considers reviews, user ratings, feature words, and item titles to generate high-quality personalized explanations. The EPER model employs a masking mechanism to prevent interference between rating prediction and explanation generation. Moreover, we propose an innovative feature-handling method to manage missing interaction features in existing models. Experimental results on public datasets demonstrate that EPER generally outperforms other well-known methods, including NETE, PETER+, and MMCT. Compared with MMCT, EPER improves explanation quality (ROUGE metric) by 3.27%, personalization (FMR metric) by 6.82%, and rating prediction (MSE metric) by 1.2% for the Amazon Clothing dataset. Overall, the EPER model provides personalized recommendation explanations that match or exceed the best existing methods, demonstrating its potential for practical applications.