Discovery of Points of Interest with Different Granularities for Tour Recommendation Using a City Adaptive Clustering Framework

Abstract

Increasing demand for personalized tours for tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity of POI has been discussed to provide more detailed information for tour planning, which supports both inside and outside routes that would improve tourists' travel experience. Such tour recommendation systems require a predefined POI database with different granularities, but existing POI discovery methods do not consider the granularity of POI well and treat all POIs as the same scale. On the other hand, the parameters also need to be tuned for different cities, which is not a trivial process. To this end, we propose a city adaptive clustering framework for discovering POIs with different granularities in this article. Our proposed method takes advantage of two clustering algorithms and is adaptive to different cities due to automatic identification of suitable parameters for different datasets. Experiments on two real-world social image datasets reveal the effectiveness of our proposed framework. Finally, the discovered POIs with two levels of granularity are successfully applied on inner and outside tour planning.