Construction and Validation of Hotel Personalized Service Recommendation Model Based on Big Data Analysis

This paper proposes a new recommendation model for personalized hotel services and discusses how to address some of the key challenges related to big data analytics and achieve the appropriate trade-offs among recommendation accuracy, user privacy, and computational efficiency. The model discussed here integrates a holistic analysis framework for customer preference analysis and a two-stage recommendation algorithm that is based on explicit and implicit customer feedback. Advanced feature engineering methods and customer segmentation approaches have identified complex user preferences and service characteristics successfully. It follows the five-layer architecture in system architecture, integrated with advanced privacy-preserving mechanisms through differential privacy using adaptive noise injection. Experimental evaluation on real-world datasets from multiple hotel chains shows that the proposed model outperforms with 92% precision and 89% recall, way ahead of state-of-the-art performance. Ablation studies show that the feature extraction mechanisms are the most contributing, with a gain of 42% among all components. Thus, this paper justifies the practical applicability of the model, tested with comprehensive categories of hotels scaling up to be highly effective in a commercial environment. Thus, this work bridges the gap between theory and practice in the hospitality industry and lays the foundation for further research on efficient and personalized service recommendations that allow for user privacy preservation and operational efficiency.