Context-aware tourist trip recommendations

Mobile and web-based services solving common tourist trip design problems are available, but only few solutions consider context for the recommendation of point of interest (POI) sequences. In this paper, we present a novel approach to incorporating context into a tourist trip recommendation algorithm. In addition to traditional context factors in tourism, such as location, weather or opening hours, we focus on two context factors that are highly relevant when recommending a sequence of POIs: time of the day and previously visited point of interest. We conducted an online questionnaire to determine the influence of the context factors on the user's decision of visiting a POI and the ratings of the POIs under these conditions. We integrated our approach into a web application recommending context-aware tourist trips across the world. In a user study, we verified the results of our novel approach as well as the application's usability. The study proves a high usability of our system and shows that our context-aware approach outperforms a baseline algorithm.