  In many online applications interactions between a user and a web-service are
organized in a sequential way, e.g., user browsing an e-commerce website. In
this setting, recommendation system acts throughout user navigation by showing
items. Previous works have addressed this recommendation setup through the task
of predicting the next item user will interact with. In particular, Recurrent
Neural Networks (RNNs) has been shown to achieve substantial improvements over
collaborative filtering baselines. In this paper, we consider interactions
triggered by the recommendations of deployed recommender system in addition to
browsing behavior. Indeed, it is reported that in online services interactions
with recommendations represent up to 30\% of total interactions. Moreover, in
practice, recommender system can greatly influence user behavior by promoting
specific items. In this paper, we extend the RNN modeling framework by taking
into account user interaction with recommended items. We propose and evaluate
RNN architectures that consist of the recommendation action module and the
state-action fusion module. Using real-world large-scale datasets we
demonstrate improved performance on the next item prediction task compared to
the baselines.
