Coactive Critiquing: Elicitation of Preferences and Features
When faced with complex choices, users refine their own preference criteria as they explore the catalogue of options. In this paper we propose an approach to preference elicitation suited for this scenario. We extend Coactive Learning, which iteratively collects manipulative feedback, to optionally query example critiques. User critiques are integrated into the learning model by dynamically extending the feature space. Our formulation natively supports constructive learning tasks, where the option catalogue is generated on-the-fly. We present an upper bound on the average regret suffered by the learner. Our empirical analysis highlights the promise of our approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Decomposition Strategies for Constructive Preference Elicitation
We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the…
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on…
Positive and Negative Critiquing for VAE-based Recommenders
Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, …
Untangle: Critiquing Disentangled Recommendations
The core principle behind most collaborative filtering methods is to embed users and items in latent spaces, where individual dimensions are learned independently of any particular item attributes. It is thus difficult f…
Collaborative FilteringMind the Gap: How Elicitation Protocols Shape the Stated-Revealed Preference Gap in Language Models
Recent work identifies a stated-revealed (SvR) preference gap in language models (LMs): a mismatch between the values models endorse and the choices they make in context. Existing evaluations rely heavily on binary force…