User Profiling for Tourist Trip Recommendations using Social Sensing

Abstract

For Point of Interest (POI) recommendations or touristic orienteering applications, it is essential to explore the user preference or potential interests to predict potential POIs or touristic routes. One of the state-of-the-art approaches for inferring the user’s interests is social sensing, which is based on implicit feedback and semantic similarities to extract the user preference. In this paper, we profile the user preferences by social sensing to exploit the similarity between users’ reviews and POI descriptions. The experiment is based on the "Yelp!" dataset and provides the labels associated with different businesses considered in the "Yelp!" Social Network. The preliminary results show that the proposed model can automatically estimate the user interests and improve the effectiveness of the user profiling procedure. The results also indicate that our proposed model can offer a foundational approach for ranking POIs and touristic routes.