An Innovative Personalized Recommendation Approach Based on Deep Learning and User Review Content

In the recent advancements of recommendation systems, the integration of deep learning models has significantly enhanced prediction accuracy and user experience. This paper introduces a novel model called GRATB, which enhances the existing BERT4Rec framework by incorporating a self-attention mechanism based on time-mixing attention mechanism and dynamic head fusion. Additionally, it introduces a new 'comment tower' to integrate user review information. By employing a hybrid approach, our model is able to incorporate both the interaction sequence between users and items, as well as sentiment-rich review data. This enables us to obtain a more comprehensive understanding of user preferences. Empirical evidence derived from extensive experimentation demonstrates that GRATB exhibits substantial performance improvements over existing state-of-the-art models across multiple evaluation metrics. GRATB achieves significant enhancements in two critical performance indicators: Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), with average increases of 2.41% and 3.25%, respectively.