Emotion and sentiment enriched decision transformer for personalized recommendations - Sana Abakarim; Sara Qassimi; Said Rakrak (Scientific Reports)
Abstract In the era of smart cities and personalized services, recommendation systems are an integral part of handling massive user-generated content and providing personalized recommendations. Traditional systems often fail to capture the nuances of embedded emotions and sentiment in user reviews, crucial to enhancing user engagement and satisfaction. Thereby, we propose the Emotion and Sentiment-Enriched Decision Transformer (ESE-DT), a novel framework that integrates sentiment and emotion analysis into sequential recommendation modeling. Based on the state-of-the-art sequence modeling framework of Decision Transformer (DT) originally developed for reinforcement learning, ESE-DT reimagines recommendation tasks as trajectory modeling problems. By treating user interactions as sequences of states, actions, and rewards, DT allows ESE-DT to learn temporal dynamics, enabling alignment between recommendations and long-term user engagement goals. This is further enhanced by the advanced NLP for sentiment and emotion extraction to improve the contextual understanding in user and item embeddings. We experimentally validate the proposed approach on two real-world datasets, namely, Yelp and Google Local Reviews. Results clearly show that ESE-DT outperforms state-of-the-art baselines along several key metrics, achieving up to 11.76% improvement in nDCG@10, 11.58% increase in HR@10, and a 42.11% reduction in RMSE on the Yelp dataset, while attaining 2.13% improvement in nDCG@10, 13.11% increase in HR@10, and a 17.43% reduction in RMSE on the Google Local Reviews dataset. These findings highlight the transformative potential of ESE-DT in advancing personalized recommendation systems by integrating emotion and sentiment analysis with the powerful capabilities of the Decision Transformer.