  A common task for recommender systems is to build a pro le of the interests
of a user from items in their browsing history and later to recommend items to
the user from the same catalog. The users' behavior consists of two parts: the
sequence of items that they viewed without intervention (the organic part) and
the sequences of items recommended to them and their outcome (the bandit part).
In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a
probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order
to improve the estimation of recommendation quality. The bandit signal is
valuable as it gives direct feedback of recommendation performance, but the
signal quality is very uneven, as it is highly concentrated on the
recommendations deemed optimal by the past version of the recom-mender system.
In contrast, the organic signal is typically strong and covers most items, but
is not always relevant to the recommendation task. In order to leverage the
organic signal to e ciently learn the bandit signal in a Bayesian model we
identify three fundamental types of distances, namely action-history,
action-action and history-history distances. We implement a scalable
approximation of the full model using variational auto-encoders and the local
re-paramerization trick. We show using extensive simulation studies that our
method out-performs or matches the value of both state-of-the-art organic-based
recommendation algorithms, and of bandit-based methods (both value and
policy-based) both in organic and bandit-rich environments.
