POI Recommendation

Point-of-Interest (POI) recommendation aims to predict users' next location or suggest personalized itineraries, leveraging location data from social networks and other sources. Current research emphasizes incorporating temporal dynamics (e.g., time-of-day preferences), user context (e.g., sentiment analysis of reviews), and collaborative filtering (e.g., leveraging global trajectory patterns) into models, often employing neural networks like transformers and graph neural networks. These advancements improve recommendation accuracy and address challenges like data sparsity and cold-start problems, ultimately enhancing user experience in location-based services and informing urban planning.