A picture-based approach for travel recommendations

Personalized recommendation strongly relies on an accurate model to capture user preferences; eliciting this information is, in general, a hard problem. In the field of tourism this initial profiling becomes even more challenging. It has been shown that particularly in the beginning of the travel decision making process, users themselves are often not conscious of their needs and are not able to express them. Aiming at revealing implicitly given user preferences, this work introduces an approach that utilizes a set of travel related pictures to discover users´ travel behavior and in turn, to deliver recommendations. Users first select three to seven pictures they like when thinking about their future travels. Then the selected pictures are mapped onto seven preference factors that reflect different travel behavioral aspects. The scores of the factors are used to recommend touristic point of interests. The pilot study shows that users are more motivated and satisfied using this non-verbal way of interaction. This talk discusses a stream of studies to quantify intangible user preferences and provide an easy and playful method to generate inputs/data for recommendation systems.