  In recent years, the emerging topics of recommender systems that take
advantage of natural language processing techniques have attracted much
attention, and one of their applications is the Conversational Recommender
System (CRS). Unlike traditional recommender systems with content-based and
collaborative filtering approaches, CRS learns and models user's preferences
through interactive dialogue conversations. In this work, we provide a
summarization of the recent evolution of CRS, where deep learning approaches
are applied to CRS and have produced fruitful results. We first analyze the
research problems and present key challenges in the development of Deep
Conversational Recommender Systems (DCRS), then present the current state of
the field taken from the most recent researches, including the most common deep
learning models that benefit DCRS. Finally, we discuss future directions for
this vibrant area.
