FACF: fuzzy areas-based collaborative filtering for point-of-interest recommendation

Several online social networks collect information from their users' interactions (co-tagging of photos, co-rating of products, etc.) producing a large amount of activity-based data. As a consequence, this kind of information is used by these social networks to provide their users with recommendations about new products or friends. Moreover, recommendation systems (RS) become capable to predict a person's activity with no special infrastructure or hardware, such as RFID tags, or by using video and audio. In that sense, we propose a technique to provide personalised points-of-interest (POIs) recommendations for users of location-based social networks (LBSNs). Our technique assumes users' preferences can be characterised by their visited locations, which is shared by them on LBSN, collaboratively exposing important features as, for instance, areas-of-interest (AOIs) and POIs popularity. Therefore, our technique, named fuzzy areas-based collaborative filtering, uses users' activities to model their preferences and recommend the next visits to them. We have performed experiments over two real LBSN datasets and the obtained results have shown our technique outperforms location collaborative filtering at almost all of the experimental evaluation. Therefore, by fuzzy clustering of AOIs, FACF is suitable to check the popularity of POIs to improve POIs recommendation.