Recommending and Planning Trip Itineraries for Individual Travellers and Groups of Tourists

Trip planning is both challenging and tedious for tourists due to their unique interest preferences and various trip constraints. Despite the availability of online resources for tour planning and services provided by tour agencies, there are various challenges such as: (i) selecting POIs that are personalized to the unique interests of individual travellers; (ii) constructing these POIs as an itinerary, with considerations for time availability and starting/ending place preferences (e.g., near a tourist’s hotel); (iii) for tour agencies to group tourists into tour groups such that the recommended tour appeals to the interests of the group as a whole; and (iv) similarly, for tour agencies to assign tour guides with the right expertise to lead each of these tour groups. In our work, we aim to develop algorithms for recommending personalized tours to both individual travellers and groups of tourists, based on their interest preferences, which we automatically determine based on geotagged photos posted by these tourists. Using a Flickr dataset of geo-tagged photos as ground-truth for reallife POI visits in multiple cities, we evaluate our proposed algorithms using various metrics such as precision, recall, F1-score, user interest scores and POI popularity, among others.