  Eliciting the preferences and needs of tourists is challenging, since people
often have difficulties to explicitly express them, especially in the initial
phase of travel planning. Recommender systems employed at the early stage of
planning can therefore be very beneficial to the general satisfaction of a
user. Previous studies have explored pictures as a tool of communication and as
a way to implicitly deduce a traveller's preferences and needs. In this paper,
we conduct a user study to verify previous claims and conceptual work on the
feasibility of modelling travel interests from a selection of a user's
pictures. We utilize fine-tuned convolutional neural networks to compute a
vector representation of a picture, where each dimension corresponds to a
travel behavioural pattern from the traditional Seven-Factor model. In our
study, we followed strict privacy principles and did not save uploaded pictures
after computing their vector representation. We aggregate the representations
of the pictures of a user into a single user representation, i.e., touristic
profile, using different strategies. In our user study with 81 participants, we
let users adjust the predicted touristic profile and confirm the usefulness of
our approach. Our results show that given a collection of pictures the
touristic profile of a user can be determined.
