  An ultimate goal of recommender systems (RS) is to improve user engagement.
Reinforcement learning (RL) is a promising paradigm for this goal, as it
directly optimizes overall performance of sequential recommendation. However,
many existing RL-based approaches induce huge computational overhead, because
they require not only the recommended items but also all other candidate items
to be stored. This paper proposes an efficient alternative that does not
require the candidate items. The idea is to model the correlation between user
engagement and items directly from data. Moreover, the proposed approach
consider randomness in user feedback and termination behavior, which are
ubiquitous for RS but rarely discussed in RL-based prior work. With online A/B
experiments on real-world RS, we confirm the efficacy of the proposed approach
and the importance of modeling the two types of randomness.
