I-Corps: SmartMenu

通过信息影响消费者行为是说服技术的目标之一。这个项目是设计餐厅点餐界面上选择的呈现和时间，以影响用餐者的行为。该团队将使用专有算法，根据用餐者的个人资料和他们过去的点餐行为，提出个性化的用餐建议。该算法是从之前由美国国家科学基金会资助的研究中发展而来的，该研究旨在为食客提供即时营养建议。人们相信，在智能推荐引擎的支持下，复杂的用餐电脑界面将提高餐厅经营者的底线，并为用餐者提供卓越的点餐体验。例如，在最近的一项研究中，该团队在一家快餐店部署了一个自助点餐终端，根据用餐者的健康目标推荐食物组合。目标可能是减肥、增肌或控制糖尿病。结果，通过增加使用该系统的食客的个人门票金额，增加了餐厅的销售额。目前的项目旨在通过在食客的基础上进一步个性化，提高系统提出的基于健康的建议的质量。S过去的订购记录。现有的销售点系统侧重于降低与经营餐厅相关的运营成本。该团队有机会为餐饮业提供前端技术，而不是通过向用餐者提供与上下文相关的用餐建议来增加餐厅的销售。该项目有可能影响在餐厅或自助餐厅设置的所有订单，包括通过网络和移动界面下的订单。根据美国国家餐馆协会的数据，2010年美国有96万家餐馆。据美国有线电视新闻网报道，美国人在那一年平均去餐馆193次。美国国家餐馆协会的一项调查发现，营养食品是最重要的消费趋势。研究结果表明，通过这样的点餐界面为用餐者提供健康的食物建议，可以对当前肥胖和与饮食相关的慢性病流行产生巨大的影响。消费者需求并不是这一趋势的唯一驱动力；新的联邦指导方针将于2011年12月生效，要求所有拥有超过20家分店的连锁餐馆在其所有标准菜单上提供完整的营养信息。在餐厅点餐会产生关于消费者用餐行为的重要数据，这些数据要么没有被捕获，要么被捕获但从未被利用。提出的自助点餐终端（SmartMenus）可以获取匿名的用餐者数据，这些数据很容易用于商业和学术研究。

Influencing consumer behavior through information is one of the goals of persuasive technology. This project is to design the presentation and timing of choices on restaurant-ordering interfaces to influence diner behavior. Using proprietary algorithms, the team will make personalized meal recommendations based both on a diner's profile and on their past ordering behavior. The algorithms evolved from previous NSF funded research on providing diners with in-the-moment nutritional advice. It is believed that sophisticated diner-computer interfaces, backed by intelligent recommendation engines, will improve a restaurant operator's bottom line and deliver a superior ordering experience for diners. For example, in a recent study the team deployed a self-service ordering terminal at a Quick Service Restaurant that recommended food combinations based on a diner's health goal. Goals might be to lose weight, build muscle or manage diabetes. The result was increased sales for the restaurant by increasing the individual ticket amount for diners using the system. The current project aims to improve the quality of the health-based recommendations made by the system by personalizing them further on the basis of a diner?s past ordering history. Existing Point-of-Sale systems focus on reducing operating costs associated with running a restaurant. The team has an opportunity to supply the restaurant industry with front-end technologies that instead focus on increasing restaurant sales by making contextually relevant meal recommendations to diners. This project has the potential to influence all the orders placed in a restaurant or cafeteria setting including those placed through web and mobile interfaces. According to the National Restaurant Association there were 960,000 restaurant locations in the USA in 2010. CNN reported that, on average, Americans visited a restaurant 193 times in that year. A survey by the National Restaurant Association found that nutrition is a top consumer trend. Study results indicate that providing diners with healthy food suggestions through such ordering interfaces can have a huge impact on the current obesity and diet-related chronic disease epidemic. Consumer demand is not the only driver of this trend; new federal guidelines that will go into effect in December of 2011 mandate that all chain restaurants with more than 20 locations provide complete nutritional information on all of their standard menu items. Ordering at a restaurant generates significant data about consumer dining behavior that is either not captured or is captured but never utilized. The proposed self-service ordering terminals (SmartMenus) capture anonymized diner data, which is easily available for commercial and academic research.