Coactive Critiquing: Elicitation of Preferences and Features
This work addresses preference refinement for users in interactive systems, but it appears incremental as it builds on existing Coactive Learning methods.
The paper tackles the problem of preference elicitation in complex choice scenarios by extending Coactive Learning to incorporate user critiques, dynamically expanding the feature space, and supporting constructive learning tasks, with results including an upper bound on average regret and empirical promise.
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.