  Recommender systems are an important part of the modern human experience
whose influence ranges from the food we eat to the news we read. Yet, there is
still debate as to what extent recommendation platforms are aligned with the
user goals. A core issue fueling this debate is the challenge of inferring a
user utility based on engagement signals such as likes, shares, watch time
etc., which are the primary metric used by platforms to optimize content. This
is because users utility-driven decision-processes (which we refer to as
System-2), e.g., reading news that are relevant for them, are often confounded
by their impulsive decision-processes (which we refer to as System-1), e.g.,
spend time on click-bait news. As a result, it is difficult to infer whether an
observed engagement is utility-driven or impulse-driven. In this paper we
explore a new approach to recommender systems where we infer user utility based
on their return probability to the platform rather than engagement signals. Our
intuition is that users tend to return to a platform in the long run if it
creates utility for them, while pure engagement-driven interactions that do not
add utility, may affect user return in the short term but will not have a
lasting effect. We propose a generative model in which past content
interactions impact the arrival rates of users based on a self-exciting Hawkes
process. These arrival rates to the platform are a combination of both System-1
and System-2 decision processes. The System-2 arrival intensity depends on the
utility and has a long lasting effect, while the System-1 intensity depends on
the instantaneous gratification and tends to vanish rapidly. We show
analytically that given samples it is possible to disentangle System-1 and
System-2 and allow content optimization based on user utility. We conduct
experiments on synthetic data to demonstrate the effectiveness of our approach.
