Eliciting Touristic Profiles: A User Study on Picture Collections
This work addresses the problem of implicit preference elicitation for tourists in early travel planning, though it is incremental as it verifies previous claims with a user study.
The paper tackled the challenge of eliciting tourist preferences by using fine-tuned convolutional neural networks to model travel interests from user-uploaded pictures, achieving a touristic profile determination with 81 participants confirming its usefulness.
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.