Compound critiques for conversational recommender systems

Recommender systems bring together ideas from information retrieval and filtering, user profiling, adaptive interfaces and machine learning in an attempt to offer users more personalized and responsive search systems. Conversational recommenders guide a user through a sequence of iterations, suggesting specific items, and using feedback from users to refine their suggestions in subsequent iterations. Different recommender systems look for different types of feedback from users. In this paper we examine the role of critiquing, a form of feedback in which the user indicates a preference over a particular feature of a recommended item. For example, when shopping for a PC a user might indicate that they like the current suggestion but they are looking for something "cheaper"; "cheaper" is a critique over the price feature of the PC case. Sometimes it is useful to critique multiple features simultaneously (compound critiques). In this paper we describe how a recommender can automatically discover useful compound critiques during the recommendation session and how these critiques can be used to improve recommendation efficiency.