Intelligent Point-of-Interest Recommendation for Tourism Planning via Density-based Clustering and Genetic Algorithm

In recent years, geographic information service and relevant social media become more popular, some geographic point may interest people, e.g. scenic spot or famous store, naming as a point-of-interest (POI). However, the number of POI contributing by social media grows exponentially which causing a searching problem. How to recommend a POI to a user/tourist becomes a challenge. This study proposes an intelligent system using density-based clustering and genetic algorithm to recommend a POIs solution for tourism planning. Density-based clustering identifies candidate POIs. Skyline method decides a superior POI from candidate POIs by dominant of multiple attributes. Genetic algorithm optimizes the recommendation solution. The contribution is to get a tourism POI solution from a huge amount of candidate POIs based on user/tourist preferences. An experimental system implementation is in progress. In future, we will use open data from Google map and Foursquare to proof the proposed system mechanism effectiveness