Analyzing Visitor Behavior to Enhance Personalized Experiences in Smart Museums: A Systematic Literature Review

This systematic review provides an analysis of information gathered from 33 chosen publications during the past decade. The analysis reveals the primary methodologies applied and identifies the visitor behaviors that enable personalized content delivery. Statistical and Data Analysis is the predominant methodology in the reviewed publications. The methodology is present in 97% of the publications. AI and Machine Learning (63.6%) and Mobile/Interactive Technologies (60.6%) are most frequently paired with this methodology. Behavioral Analytics Platforms and Mobile/Wearable Devices are the most used technologies (42.4%) for delivering personalized content. A total of 39.4% of publications utilize Location Tracking Systems. The most frequent visitor behavior analysis focuses on Interactive Engagement and Movement Patterns, which occur 72.7% of the time, before Learning Patterns and Physical Positioning, which occur 63.6% of the time. The behavioral analysis of Group Dynamics (27.3%) and Emotional Response (18.2%) represents the least common practice when museums personalize their content despite the significance of social interaction analysis among visitors. The leading content personalization methods currently include real-time personalization systems combined with AI-driven systems and location-based technologies. Personalized content delivery systems face challenges including privacy protection and scalability issues paired with expensive implementation costs, which especially affect smaller museums. Researchers should explore how new technologies, such as virtual reality, augmented reality, and advanced biometric systems, can be integrated into future developments.