Artificial Intelligence Simulation of Collaborative Filtering Recommendation Algorithm Based on Big Data Network Security in Tourism Behavior Analysis

ABSTRACT

In the tourism industry, massive data such as tourist behavior patterns and preference information is collected and analyzed to enhance travel experiences and optimize service delivery. However, risks of data leakage, tampering, and misuse during storage, transmission, and processing pose serious threats to both tourists “privacy and tourism enterprises” commercial interests. This study investigates cybersecurity protection technologies in big data environments, including data encryption, access control, and anomaly detection, to ensure tourist data security throughout collection, storage, and transmission processes. A novel three‐dimensional facial expression analysis algorithm based on emotion recognition is proposed, with optimized algorithms enhancing both accuracy and robustness. The research also examines collaborative filtering recommendation algorithms and introduces an improved cosine similarity algorithm to improve recommendation system security and reliability. A behavioral analysis model for tourism target customer groups was developed, with experimental validation demonstrating its performance in network information security environments. Results show that the enhanced collaborative filtering recommendation algorithm outperforms traditional methods in recommendation accuracy and recall rate, while exhibiting higher robustness under cyber attack scenarios. This innovation effectively safeguards tourists' privacy and data security, while providing tourism enterprises with precise behavioral analytics and decision support, carrying significant theoretical and practical implications.