Rendezfood: A Design Case Study of a Conversational Location-based Approach in Restaurants

2025-01-07

AI Summary

The foodservice industry faces mounting challenges, including margin compression from third-party delivery platforms, lagging technological adoption, and low user engagement. To address these, this study proposes a novel Human-Food Interaction (HFI) platform integrating Location-Based Services (LBS) with conversational AI to enhance local restaurant dish visibility and foster affective user–food connections. Methodologically, we developed a location-aware, multi-turn conversational agent and employed co-design alongside participatory field evaluations involving both end users and restaurant owners. Our contributions are threefold: (1) the first deep integration of LBS and conversational AI specifically for foodservice contexts; (2) an expansion of HFI design dimensions beyond functional utility toward affective and contextual engagement; and (3) a pragmatic exploration of reality-oriented metaverse applications in local life services. Empirical evaluation demonstrates significant improvements in user discovery intent and emotional connection; identifies critical bottlenecks in contextually grounded content generation; and yields a reusable requirements framework and validated design prototype.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Application Domains: Humanities & Computational Social Science
• Intelligent Robots: Human-Robot Interaction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The restaurant industry is currently facing a challenging socio-economic situation caused by the rise of delivery services, inflation, and typically low margins. Often, technological opportunities for process optimization or customer retention are not fully utilized. In our design case study, we investigate which technologies are already being used to improve the customer experience in restaurants and explore a novel new approach to this issue. We designed, implemented, and evaluated a platform with customers and restaurateurs to increase visibility and emotional connection to nearby restaurants through their dishes. Some of our key findings include the enormous potential of combining location-based systems and conversational agents, but also the difficulties in creating content for such platforms. We contribute to the field of Human-Food Interaction by (1) identifying promising design spaces as well as customer and restaurateur requirements for technology in this domain, (2) presenting an innovative design case study to improve the user experience, and (3) exploring the broader implications of our design case study findings for approaching a real-world metaverse.

Problem

• Catering Industry
• Technology Application
• Customer Experience Enhancement

Innovation

• Rendezfood Platform
• Location-based Services
• Chatbot Technology