Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system

In a recommender system that suggests options based on user attribute weights, the method of preference elicitation (PE) employed by a recommender system can influence users' satisfaction with the system, as well as the perceived usefulness and the understandability of the system. Specifically, we hypothesize that users with different levels of domain knowledge prefer different types of PE. While domain experts reported higher satisfaction and perceived usefulness with attribute-based PE (i.e., indicating preference levels for the domain-related attributes), novices preferred case-based PE (i.e., indicating the preference for specific examples, from which attribute- preferences can then be implicitly calculated). The paper discusses the decision-theoretical principles that are believed to lead to this distinction, as well as an experiment that provides substantial evidence for the hypothesis. Consequently, we introduce the idea of adapting the method of PE to users' domain knowledge on the fly using click stream data. Copyright 2009 ACM.