A hybrid system for personalized next point-of-interest recommendations using deep learning and fuzzy logic

We propose TourOptiGuide, a hybrid tourism recommendation prototype that integrates deep learning and fuzzy logic for context-aware point-of-interest (POI) suggestion. The system generates next-POI recommendations by jointly considering four factors: the tourist’s current location, inferred preferences, estimated age, and historical visit context. Deep learning models are employed to detect POIs from images and estimate tourist age from facial images, while a trajectory data warehouse structures historical visit information for contextual filtering. A fuzzy inference system is used to combine these heterogeneous inputs and produce interpretable recommendation decisions under uncertainty. The proposed framework is evaluated as a proof-of-concept prototype, demonstrating coherent system behavior and functional integration of perception, historical context, and fuzzy reasoning.