  Recommender systems are software applications that help users to find items
of interest in situations of information overload. Current research often
assumes a one-shot interaction paradigm, where the users' preferences are
estimated based on past observed behavior and where the presentation of a
ranked list of suggestions is the main, one-directional form of user
interaction. Conversational recommender systems (CRS) take a different approach
and support a richer set of interactions. These interactions can, for example,
help to improve the preference elicitation process or allow the user to ask
questions about the recommendations and to give feedback. The interest in CRS
has significantly increased in the past few years. This development is mainly
due to the significant progress in the area of natural language processing, the
emergence of new voice-controlled home assistants, and the increased use of
chatbot technology. With this paper, we provide a detailed survey of existing
approaches to conversational recommendation. We categorize these approaches in
various dimensions, e.g., in terms of the supported user intents or the
knowledge they use in the background. Moreover, we discuss technological
approaches, review how CRS are evaluated, and finally identify a number of gaps
that deserve more research in the future.
