  Recommender systems associated with social networks often use social
explanations (e.g. "X, Y and 2 friends like this") to support the
recommendations. We present a study of the effects of these social explanations
in a music recommendation context. We start with an experiment with 237 users,
in which we show explanations with varying levels of social information and
analyze their effect on users' decisions. We distinguish between two key
decisions: the likelihood of checking out the recommended artist, and the
actual rating of the artist based on listening to several songs. We find that
while the explanations do have some influence on the likelihood, there is
little correlation between the likelihood and actual (listening) rating for the
same artist. Based on these insights, we present a generative probabilistic
model that explains the interplay between explanations and background
information on music preferences, and how that leads to a final likelihood
rating for an artist. Acknowledging the impact of explanations, we discuss a
general recommendation framework that models external informational elements in
the recommendation interface, in addition to inherent preferences of users.
