基于多模态深度学习的共享短租重复入住预测模型

How to improve customer retention and establish a stable platform-customer trust relationship has become a major challenge for online short-term rental platform operators and housing providers. This study proposes a prediction model for Airbnb guest repeat stays based on multimodal deep learning. According to the trust mechanism and service experience and other related theories, multimodal features are extracted, and a deep learning model integrating numeric, enumeration, text and image multimodal data processing is proposed. In the data including tens of thousands of repeated check-in behaviors of tens of thousands of guests, the proposed method reaches or approaches 90% in the three indicators of accuracy, recall rate and F-value, and also confirms that the prediction effect based on multimodal data is significantly better than that of single-modal data. This study provides certain theoretical and methodological guidance for platform operators and property providers to increase customer repeat stays, and the relevant prediction methods are also innovative.