  Social influence plays a vital role in shaping a user's behavior in online
communities dealing with items of fine taste like movies, food, and beer. For
online recommendation, this implies that users' preferences and ratings are
influenced due to other individuals. Given only time-stamped reviews of users,
can we find out who-influences-whom, and characteristics of the underlying
influence network? Can we use this network to improve recommendation?
  While prior works in social-aware recommendation have leveraged social
interaction by considering the observed social network of users, many
communities like Amazon, Beeradvocate, and Ratebeer do not have explicit
user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic
graphical model, to automatically learn the latent influence network underlying
a review community -- given only the temporal traces (timestamps) of users'
posts and their content. Based on extensive experiments with four real-world
datasets with 13 million reviews, we show that GhostLink improves item
recommendation by around 23% over state-of-the-art methods that do not consider
this influence. As additional use-cases, we show that GhostLink can be used to
differentiate between users' latent preferences and influenced ones, as well as
to detect influential users based on the learned influence graph.
