Abstract Personalized recommendations have become a ubiquitous part of our online user experience. Today, recommendations are commonly implemented as a one-directional communication from the system to the user. However, in recent years, we observed an increased interest in conversational recommender systems (CRS). These systems are able to sustain an interactive dialogue with users, often in natural language, with the goal of providing suitable recommendations based on the users’ observed needs and preferences. While conversational recommendation is not a new field, recent developments in natural language processing technology and in deep learning have significantly spurred new research in this area.

In this tutorial, we will provide a multi-faceted survey on existing research in the area of conversational recommender systems. We will first discuss typical technical architectures and the possible interaction modalities for CRS. Then, we will focus on the various types of knowledge these systems can rely on and elaborate on the computational tasks such systems usually have to support. In the final parts of the tutorial, we emphasize on current approaches and the open challenges when evaluating complex interactive software solutions like conversational recommender systems.