文献类型：期刊文献

英文题名：Research on Optimization of Personalized Tourism Recommendation System Driven by Artificial Intelligence

作者：Zeng, Chao[1];Wu, Chunhuan[2]

第一作者：Zeng, Chao

通讯作者：Wu, CH[1]

机构：[1]Nanjing Univ Ind Technol, Sch Econ & Management, Nanjing, Peoples R China;[2]Beijing Union Univ, Tourism Coll, Beijing, Peoples R China

第一机构：Nanjing Univ Ind Technol, Sch Econ & Management, Nanjing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Tourism Coll, Beijing, Peoples R China.|[1141732]北京联合大学旅游学院;[11417]北京联合大学;

年份：2025

卷号：16

期号：1

外文期刊名：INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS IN THE SERVICE SECTOR

收录：EI(收录号：20260519977200);WOS:【ESCI(收录号:WOS:001676865000001)】；

基金：This research was supported by Beijing Union University's Teaching reform project "Curriculum Reform and Practice of Literature Search and Review Based on the Background of Digitization" (grant number: JJ2024Y021) and National Natural Science Foundation of China Project "study on place meaning identification, construction path selection and driving mechanism of abandoned mining areas under recreational utilization" (grant number: 42271255).

语种：英文

外文关键词：Artificial Intelligence; Tourism; Personalization; Recommendation System; Optimize

摘要：With smart tourism shifting from "search-price comparison" to "search-and-order," platforms must respond within milliseconds to dynamic contexts like weather and traffic. Using 2.4 million user logs and 20,000 questionnaires, the authors propose a three-level architecture: (1) a multi-granularity Transformer-Encoder unifies long-term interests and short-term intent via spatiotemporal attention;(2) a gradient-aligned distillation layer compresses high-dimensional sparse context into 512 dimensions, achieving 42ms latency with 97% entropy retention; and (3) a Pareto-aware Contextual Bandit dynamically balances CTR, conversion, and merchant fairness. Experiments show that the new framework is improved by 12.4% and 9.7% on NDCG@10 and MAP@20, respectively. The 14-day online A/B test shows that CTR is improved by 15.3%, the order conversion rate is improved by 9.8%. The recall rate of cold start attractions can still be maintained at 0.43. This study provides a low-cost and portable paradigm and lays the foundation for real-time context modeling of smart tourism.