Analyzing Influential Factors in Review-Based Restaurant Recommender Systems: The Role of Review Length, Aspect, and Emotion

Review text in recommender systems provides rich insights into user preferences and experiences that cannot be fully captured by numerical ratings alone. While recent studies have increasingly leveraged review text to enhance recommendation accuracy, most have primarily focused on improving model performance, with limited attention to quantitatively examining how specific textual elements influence rating prediction. To address this gap, this study empirically investigates the impact of review t