A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining

Planning a personalized POI route before touring a new city is an important travel preparation activity; however, it is a challenging and time-consuming task for tourists. Although some previous works focus on suggesting POI visit list or sequences, they fail to suggest personalized POI routes due to ignoring multifaceted tourism contexts. Also, they often suffer from tourist cold start or data sparsity problem because of the lack of tourism related data. To address the above weaknesses, we first propose a novel method to integrate heterogeneous tourism data collected from websites to construct a POI knowledgebase and massive structured POI visit sequences. Next, a POI-Visit sequential pattern mining algorithm is proposed to generate various fine-grained candidate POI routes from POI visit sequences while considering various tourism contexts. At the POI route recommendation stage, our system retrieve and rank a list of candidate routes according to the querying tourist's tourism contexts, including the intended travel duration, the companion type in trip, the visit season and the preferring POI tourism types, etc. In our validation experiments, we select Guilin city in China as an example to construct a real POI knowledgebase which consists of 132 POIs and 8778 POI traffic time, and construct 5694 structured POI visit sequences based on 10,109 downloaded original travelogues. The experimental results demonstrate the advantages of our system in recommending fine-grained and high personalized POI routes for specific tourists.