Itinerary Recommendation

Itinerary recommendation systems aim to create personalized travel plans by sequencing points of interest (POIs) while optimizing factors like travel time, user preferences, and crowd levels. Current research emphasizes leveraging advanced machine learning models, particularly transformer-based architectures like BERT, to generate itineraries that consider both individual tastes (based on past behavior and stated preferences) and group dynamics. This field is significant for its potential to enhance user experience in travel planning and for its contributions to the broader areas of sequential recommendation and optimization under constraints.