View article

Location-based Social Networks (LBSN) allow users to check-in in a Point-of-Interest (POI) 1 and share their activities with friends, providing publicly available data about their behavior. One of the distinctive features of LBSN data with respect to traditional location prediction systems, which are mainly based on GPS data and focus on physical mobility [33], is the rich categorization of POIs in consistent taxonomies, which a ribute an explicit semantic meaning to users’ activities. e availability of venue categories has opened new research lines, such as statistical studies of venues peculiarities [17], automatic creation of representations of city neighborhoods and users [22, 25], de nition of semantic similarities between cities [24]. Most importantly, venue categories play an important role in POI recommender systems, as they enable to model user interests and personalize the recommendations [18]. In the past years, li le a ention has been dedicated to the temporal correlations among venue categories in the exploration of a

1 e term venue is used interchangeably with POI in this work to describe an entity that has a somewhat xed and physical extension as de ned by hp://schema. org/Place city, which is nonetheless a crucial factor in recommending POIs. Consider the example of a check-in in an Irish Pub at 8 PM: is the user more likely to continue her evening in a Karaoke Bar or in an Opera House? Be er a Chinese Restaurant or an Italian Restaurant for dinner a er a City Park in the morning and a History Museum in the a ernoon? Note that predicting these sequences require an implicit modeling of at least two dimensions: 1) temporal, as certain types …