  Recommender systems exploit interaction history to estimate user preference,
having been heavily used in a wide range of industry applications. However,
static recommendation models are difficult to answer two important questions
well due to inherent shortcomings: (a) What exactly does a user like? (b) Why
does a user like an item? The shortcomings are due to the way that static
models learn user preference, i.e., without explicit instructions and active
feedback from users. The recent rise of conversational recommender systems
(CRSs) changes this situation fundamentally. In a CRS, users and the system can
dynamically communicate through natural language interactions, which provide
unprecedented opportunities to explicitly obtain the exact preference of users.
  Considerable efforts, spread across disparate settings and applications, have
been put into developing CRSs. Existing models, technologies, and evaluation
methods for CRSs are far from mature. In this paper, we provide a systematic
review of the techniques used in current CRSs. We summarize the key challenges
of developing CRSs in five directions: (1) Question-based user preference
elicitation. (2) Multi-turn conversational recommendation strategies. (3)
Dialogue understanding and generation. (4) Exploitation-exploration trade-offs.
(5) Evaluation and user simulation. These research directions involve multiple
research fields like information retrieval (IR), natural language processing
(NLP), and human-computer interaction (HCI). Based on these research
directions, we discuss some future challenges and opportunities. We provide a
road map for researchers from multiple communities to get started in this area.
We hope this survey can help to identify and address challenges in CRSs and
inspire future research.
