Eliciting Touristic Profiles: A User Study on Picture Collections

TL;DR

This study demonstrates that a user's travel interests can be effectively modeled from their picture collections using neural networks, aiding early-stage travel recommendations while respecting privacy.

Contribution

The paper validates the feasibility of deriving touristic profiles from pictures with a neural network approach and user validation, advancing implicit preference modeling.

Findings

Touristic profiles can be predicted from user pictures.

Participants confirmed the usefulness of the profile predictions.

Privacy-preserving methods were employed in the process.

Abstract

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.