  Recommendation emails are among the best ways to re-engage with customers
after they have left a website. While on-site recommendation systems focus on
finding the most relevant items for a user at the moment (right item), email
recommendations add two critical additional dimensions: who to send
recommendations to (right person) and when to send them (right time). It is
critical that a recommendation email system not send too many emails to too
many users in too short of a time-window, as users may unsubscribe from future
emails or become desensitized and ignore future emails if they receive too
many. Also, email service providers may mark such emails as spam if too many of
their users are contacted in a short time-window. Optimizing email
recommendation systems such that they can yield a maximum response rate for a
minimum number of email sends is thus critical for the long-term performance of
such a system. In this paper, we present a novel recommendation email system
that not only generates recommendations, but which also leverages a combination
of individual user activity data, as well as the behavior of the group to which
they belong, in order to determine each user's likelihood to respond to any
given set of recommendations within a given time period. In doing this, we have
effectively created a meta-recommendation system which recommends sets of
recommendations in order to optimize the aggregate response rate of the entire
system. The proposed technique has been applied successfully within
CareerBuilder's job recommendation email system to generate a 50\% increase in
total conversions while also decreasing sent emails by 72%
