Incorporating Implicit and Explicit Feature Fusion into Hybrid Recommendation for Improved Rating Prediction

Online review texts serve as a valuable source of auxiliary information for addressing the data sparsity problem in recommender systems. These reviews often reflect user preferences across multiple item attributes and can be effectively incorporated into recommendation models to enhance both the accuracy and interpretability of recommendations. Review-based recommendation approaches can be broadly classified into implicit and explicit methods. Implicit methods leverage deep learning techniques to extract latent semantic representations from review texts but generally lack interpretability due to limited transparency in the training process. In contrast, explicit methods rely on hand-crafted features derived from domain knowledge, which offer high explanatory capability but typically capture only shallow information. Integrating the complementary strengths of these two approaches presents a promising direction for improving recommendation performance. However, previous research exploring this integration remains limited. In this study, we propose a novel recommendation model that jointly considers implicit and explicit representations derived from review texts. To this end, we incorporate a self-attention mechanism to emphasize important features from each representation type and utilize Bidirectional Encoder Representations from Transformers (BERT) to capture rich contextual information embedded in the reviews. We evaluate the performance of the proposed model through extensive experiments using three real-world datasets. The experimental results demonstrate that our model outperforms several baseline models, confirming its effectiveness in generating accurate and explainable recommendations.