As a transition from offline to online shopping is taking place in many societies, many studies have been conducted to align products with user preferences. However, the existing collaborative filtering technology has a small number of user–item interactions, resulting in data sparsity and cold start problems. This study proposes a recommendation system that combines customer preference for an item with quantitative indicators. To this end, the Amazon dataset is used to quantify an item’s attribute information through Sentence-BERT, and emotion analysis of the review data is performed. The model proposed in this study simultaneously utilizes the attribute information and review data of an item, proving that it provides higher performance than when using review text alone. Finally, we verified that our approach significantly outperforms traditional baseline models and rating predictions and effectively improves top K recommendation indicators. In addition, ablation studies found that integrating item attributes and review emotions performs better than using them individually. This means that the complementary synthesis of objective item meanings and subjective user emotions can model user preferences more accurately, enabling personalized recommendations.
Lim et al. (Sat,) studied this question.