Preference Elicitation with Interdependency and User Bother Cost

Abstract

Agent-based scheduling systems, such as automated systems that schedule meetings for users and systems that schedule smart devices in smart homes, require the elicitation of user preferences in oder to operate in a manner that is consistent with user expectations. Unfortunately, interactions between such systems and users can be limited as human users prefer to not be overly bothered by such systems. As such, a key challenge is for the system to efficiently elicit key preferences without bothering the users too much. To tackle this problem, we propose a cost model that captures the cognitive or bother cost associated with asking a question. We incorporate this model into our iPLEASE system, an interactive preference elicitation approach. iPLEASE represents a user's preferences as a matrix, called preference matrix, and uses heuristics to select, from a given set of questions, an efficient sequence of questions to ask the user such that the total bother cost incurred to the user does not exceed a given bother cost budget. The user's response to those questions will partially populate the preference matrix. It then performs an exact matrix completion via convex optimization to approximate the remaining preferences that are not directly elicited. We empirically apply iPLEASE on randomly-generated problems as well as on a real-world dataset for the smart device scheduling problem to demonstrate that our approach outperforms other non-trivial benchmarks in eliciting user preferences.