Personalization without Interrogation: Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents

Software agents that provide consumers with personalized product recommendations based on individual-level feature-based preference models have been shown to facilitate better consumption choices while dramatically reducing the effort required to make these choices. This article examines why, despite their usefulness, such tools have not yet been widely adopted in the marketplace. We argue that the primary reason for this is that the usability of recommendation systems has been largely neglected – both in academic research and in practice – and we outline a roadmap for future research that might lead to recommendation agents that are more readily adopted by consumers.