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.

中文翻译：

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对话式推荐系统调查

推荐系统是一种软件应用程序，可帮助用户在信息过载的情况下找到感兴趣的项目。当前的研究通常采用一次性交互范式，其中用户的偏好是根据过去观察到的行为来估计的，并且建议排名列表的呈现是用户交互的主要单向形式。对话式推荐系统 (CRS) 采用不同的方法并支持更丰富的交互集。例如，这些交互可以帮助改进偏好获取过程或允许用户提出有关推荐的问题并提供反馈。过去几年，人们对 CRS 的兴趣显着增加。这一发展主要归功于自然语言处理领域的重大进展，新的语音控制家庭助理的出现，以及聊天机器人技术的使用增加。在本文中，我们对现有的对话式推荐方法进行了详细调查。我们在不同的维度对这些方法进行分类，例如，根据支持的用户意图或他们在后台使用的知识。此外，我们讨论了技术方法，回顾了 CRS 的评估方式，并最终确定了一些值得在未来进行更多研究的差距。