Enhancing Collaborative Filtering with Friendship Information

Liliana Ardissono, Maurizio Ferrero, Giovanna Petrone, Marino Segnan
Dipartimento di Informatica
Università di Torino
Torino, Italy
{liliana.ardissono, giovanna.petrone, marino.segnan}@unito.it
maurizio.ferrero@di.unito.it


ABSTRACT

With the convergence of social networks and recommender systems, 
new recommender algorithms have been explored, that combine observations 
on item selection with an analysis of social relations among people.
Concerning collaborative filtering approaches, we test the impact of 
integrating a measure of common friendship, in order to capture 
the intuition that socially related groups of people tend to have similar tastes. 
An experiment on the Yelp dataset shows that using preference information 
derived from the commonalities of interests in networks of friends achieves 
higher accuracy than item-to-item and other collaborative filtering algorithms.  


Keywords: Recommender systems, collaborative filtering, homophily and social networks