  Language systems have been of great interest to the research community and
have recently reached the mass market through various assistant platforms on
the web. Reinforcement Learning methods that optimize dialogue policies have
seen successes in past years and have recently been extended into methods that
personalize the dialogue, e.g. take the personal context of users into account.
These works, however, are limited to personalization to a single user with whom
they require multiple interactions and do not generalize the usage of context
across users. This work introduces a problem where a generalized usage of
context is relevant and proposes two Reinforcement Learning (RL)-based
approaches to this problem. The first approach uses a single learner and
extends the traditional POMDP formulation of dialogue state with features that
describe the user context. The second approach segments users by context and
then employs a learner per context. We compare these approaches in a benchmark
of existing non-RL and RL-based methods in three established and one novel
application domain of financial product recommendation. We compare the
influence of context and training experiences on performance and find that
learning approaches generally outperform a handcrafted gold standard.
