Unveiling Customer Preferences from Online Tourism Reviews: A Data-Driven Framework for Customer Segmentation and Hotel Recommendation

Online tourism platforms generate large volumes of customer reviews containing rich information about hotel experiences, yet customers’ heterogeneous attribute preferences remain difficult to identify and utilize in customer segmentation and hotel recommendation. To address this gap, this study proposes an online review data-driven framework for customer segmentation and hotel recommendation. Specifically, the framework employs Bert-LDA and RNTN to extract key attributes and their sentiment from online reviews. Several machine learning models then assess the nonlinear relationship between attributes and overall customer satisfaction. Notably, the Local Interpretable Model-Agnostic Explanations algorithm is applied for the first time to evaluate each attribute’s contribution to customer satisfaction in individual reviews, forming the basis for customer clustering. Next, a novel distance-based group member contribution measurement method is proposed to evaluate the differentiated attribute weights among segmented customer groups. Finally, the PLTS-VIKOR method is applied to generate differentiated recommendation lists for each group. The framework is evaluated through a case study based on 28,502 online tourism reviews. Results reveal substantial heterogeneity in attribute priorities across segments, while room quality and service remain important across most segments. The framework produces differentiated hotel rankings that reflect each segment’s distinct preferences, and provides a practical approach for hotel recommendation.