  Recommender systems aim to fulfill the user's daily demands. While most
existing research focuses on maximizing the user's engagement with the system,
it has recently been pointed out that how frequently the users come back for
the service also reflects the quality and stability of recommendations.
However, optimizing this user retention behavior is non-trivial and poses
several challenges including the intractable leave-and-return user activities,
the sparse and delayed signal, and the uncertain relations between users'
retention and their immediate feedback towards each item in the recommendation
list. In this work, we regard the retention signal as an overall estimation of
the user's end-of-session satisfaction and propose to estimate this signal
through a probabilistic flow. This flow-based modeling technique can
back-propagate the retention reward towards each recommended item in the user
session, and we show that the flow combined with traditional learning-to-rank
objectives eventually optimizes a non-discounted cumulative reward for both
immediate user feedback and user retention. We verify the effectiveness of our
method through both offline empirical studies on two public datasets and online
A/B tests in an industrial platform.
