In this paper, we present a personalized recommender system able to suggest to the target user itineraries that both meet her preferences and needs, and are sensitive to her physical and social contexts. The recommendation process takes into account different aspects: in addition to the popularity of the points of interest (POIs), inferred by considering, for instance, the number of check-ins on social networking services such as Foursquare, it also includes the user’s profile, the current context of use, and the user’s network of social ties. The system, therefore, consists of four main modules that accomplish the following tasks: (1) the construction of the user’s profile according to her interests and tastes; (2) the creation of the path graph in the user’s proximity; (3) the routing to locate the first k itineraries that match the query; (4) their ranking through a scoring function that considers the POI popularity, the user’s profile, and her physical and social context. The proposed system was evaluated on a sample of 40 real users. Experimental results showed the effectiveness of the proposed recommender.
|Titolo:||A social context-aware recommender of itineraries between relevant points of interest|
|Data di pubblicazione:||2016|
|Citazione:||D’Agostino, D., Gasparetti, F., Micarelli, A., & Sansonetti, G. (2016). A social context-aware recommender of itineraries between relevant points of interest. In Communications in Computer and Information Science (pp.354-359). Springer Verlag.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|