1. An Innovative Personalized Recommendation Approach Based on Deep Learning and User Review Content
- Author
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Zhaowang Wu, Quan Wen, Fan Yang, and Kaixin Deng
- Subjects
Recommendation systems ,deep learning ,GRATB ,BERT4Rec ,time-mixing attention mechanism ,dynamic head fusion ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
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.
- Published
- 2024
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