  User modeling is a very important task for making relevant suggestions of
venues to the users. These suggestions are often based on matching the venues'
features with the users' preferences, which can be collected from previously
visited locations. In this paper, we present a set of relevance scores for
making personalized suggestions of points of interest. These scores model each
user by focusing on the different types of information extracted from venues
that they have previously visited. In particular, we focus on scores extracted
from social information available on location-based social networks. Our
experiments, conducted on the dataset of the TREC Contextual Suggestion Track,
show that social scores are more effective than scores based venues' content.
