  Two main approaches to using social network information in recommendation
have emerged: augmenting collaborative filtering with social data and
algorithms that use only ego-centric data. We compare the two approaches using
movie and music data from Facebook, and hashtag data from Twitter. We find that
recommendation algorithms based only on friends perform no worse than those
based on the full network, even though they require much less data and
computational resources. Further, our evidence suggests that locality of
preference, or the non-random distribution of item preferences in a social
network, is a driving force behind the value of incorporating social network
information into recommender algorithms. When locality is high, as in Twitter
data, simple k-nn recommenders do better based only on friends than they do if
they draw from the entire network. These results help us understand when, and
why, social network information is likely to support recommendation systems,
and show that systems that see ego-centric slices of a complete network (such
as websites that use Facebook logins) or have computational limitations (such
as mobile devices) may profitably use ego-centric recommendation algorithms.
