SmartEats: Investigating Effects of the Customizable Conversational Agent in Dietary Recommendations

Abstract: In conversational recommender systems (CRS), the communication characteristics exhibited by the conversational agent (CA) can greatly shape user experience and their perceptions of the recommendation quality. Yet, prior work often adopts a one-size-fits-all approach, leaving the potential benefits of CA customizability—allowing users to tailor agent traits to their preferences—largely unexplored. We examine this gap in the context of dietary recommendations by introducing SmartEats, a CRS featuring a CA that can be customized by users. Through a between-subjects experiment (N = 214), we compared SmartEats to a non-customizable baseline, and followed up with participants after one week to understand whether and how the recommendations affect their food choices. We found that CA customizability directly improved participants’ immediate experience and indirectly enhanced their ability to later recall the recommendations. Reflecting on the findings, we discuss opportunities for CRS to enhance health and well-being by leveraging the customizability of emerging AI technologies.