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.
Ke, Chih-Kun; Wu, Mei-Yu; Ho, Wang-Chi; Lai, Suz-Cheng; and Huang, Li-Te, "Intelligent Point-of-Interest Recommendation for Tourism Planning via Density-based Clustering and Genetic Algorithm" (2018). PACIS 2018 Proceedings. 140.