  While liking or upvoting a post on a mobile app is easy to do, replying with
a written note is much more difficult, due to both the cognitive load of coming
up with a meaningful response as well as the mechanics of entering the text.
Here we present a novel textual reply generation model that goes beyond the
current auto-reply and predictive text entry models by taking into account the
content preferences of the user, the idiosyncrasies of their conversational
style, and even the structure of their social graph. Specifically, we have
developed two types of models for personalized user interactions: a
content-based conversation model, which makes use of location together with
user information, and a social-graph-based conversation model, which combines
content-based conversation models with social graphs.
