Personalized recommendation of points-of-interest based on multilayer local community detection

Abstract

When visiting a touristic venue, building personalized itineraries is often non-trivial, mainly because of the variety of types of points-of-interest (PoIs) that might be considered by an individual. Several online platforms exist to support the tourists by providing them with detailed PoI-related information in a certain area, such as routes, distances, reviews, and ratings. However, integrating all these aspects can be tricky, and finding a reasonable trade-off between spatial/temporal proximity, amount and serendipity of the PoIs to visit can be challenging even for expert tourists. In this work, we propose a novel approach to the recommendation of a set of PoIs for a geographic area set around a given seed PoI, by leveraging a multilayer local community detection framework. The seed-centric communities are discovered in a complex network system, whose nodes correspond to PoIs and relations in the different layers correspond to services provided by different online platforms, i.e., Google Maps, Foursquare and Wikipedia. Experimental evaluation on renowned Italian touristic venues unveiled interesting findings on the significance of the proposed approach.