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Exploiting contextual information for recommender systems oriented to tourism

The use of contextual information like geographic, temporal (including sequential), and item features in Recommender Systems has favored their development in several different domains such as music, news, or tourism, together with new ways of evaluating the generated suggestions. This paper presents the underlying research in a PhD thesis introducing some of the fundamental considerations of the current tourism-based models, emphasizing the Point-Of-Interest (POI) problem, while proposing solutions using some of these additional contexts to analyze how the recommendations are made and how to enrich them. At the same time, we also intend to redefine some of the traditional evaluation metrics using contextual information to take into consideration other complementary aspects beyond item relevance. Our preliminary results show that there is a noticeable popularity bias in the POI recommendation domain that has not been studied in detail so far; moreover, the use of contextual information (such as temporal or geographical) help us both to improve the performance of recommenders and to get better insights of the quality of provided suggestions.