  We consider the problem of recommending relevant content to users of an
internet platform in the form of lists of items, called slates. We introduce a
variational Bayesian Recurrent Neural Net recommender system that acts on time
series of interactions between the internet platform and the user, and which
scales to real world industrial situations. The recommender system is tested
both online on real users, and on an offline dataset collected from a Norwegian
web-based marketplace, FINN.no, that is made public for research. This is one
of the first publicly available datasets which includes all the slates that are
presented to users as well as which items (if any) in the slates were clicked
on. Such a data set allows us to move beyond the common assumption that
implicitly assumes that users are considering all possible items at each
interaction. Instead we build our likelihood using the items that are actually
in the slate, and evaluate the strengths and weaknesses of both approaches
theoretically and in experiments. We also introduce a hierarchical prior for
the item parameters based on group memberships. Both item parameters and user
preferences are learned probabilistically. Furthermore, we combine our model
with bandit strategies to ensure learning, and introduce `in-slate Thompson
Sampling' which makes use of the slates to maximise explorative opportunities.
We show experimentally that explorative recommender strategies perform on par
or above their greedy counterparts. Even without making use of exploration to
learn more effectively, click rates increase simply because of improved
diversity in the recommended slates.
