Recommender system is an information filtering system that helps users filter a large number of invalid information to obtain information or items by estimating their interests and preferences. The mainstream traditional recommendation system mainly uses offline and historical user data to continuously train and optimize offline models, and then recommend items for online users. There are three main problems:the unreliable estimation of user preferences based on sparse and noisy historical data, the ignorance of online contextual factors that affect user behavior, and the unreliable assumption that users are aware of their preferences by default. Since the dialogue system focuses on the user's real-time feedback data and obtains the user's current interaction intentions, "conversational recommendation" combines the interactive form of the dialogue system with the recommendation task, and becomes an effective means to solve the traditional recommendation problem. Through online interactive methods, conversational recommendation can guide and capture users' current preferences and interests, and provide timely feedback and updates. Thanks to the widespread use of voice assistants and chatbot technologies, as well as the mature application of technologies such as reinforcement learning and knowledge graphs in recommendation strategies, in the past few years, more and more researchers have paid attention to conversational recommendation systems. This survey combs the overall framework of the conversational recommendation system, classifies the datasets used in the conversational recommendation algorithm, and discusses the relevant metrics to evaluate the effect of the conversational recommendation. Focusing on the background interaction strategy and recommendation logic in conversational recommendation, this survey summarizes the existing research achievements of the domestic and foreign researchers in recent years. And finally, this survey also summarizes and prospects future works of conversational recommendation.