Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies.
Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies.
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DOI：
10.3389/frobt.2021.733835
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发表时间：
2021
影响因子：
3.4
通讯作者：
Marsella S
中科院分区：
文献类型：
--
作者：
Pecune F;Callebert L;Marsella S
不健康的饮食行为是一个重大的公共卫生问题，对个人健康有着严重的影响。克服这一问题并帮助人们改变饮食行为的一个潜在解决方案是开发能够推荐健康食谱的对话系统。此类系统面临的一个挑战是提供符合用户需求和偏好的个性化推荐。除了推荐本身的内在质量外，各种因素也可能影响用户对推荐的看法。在本文中，我们介绍了Cora，这是一个根据用户的饮食习惯和当前偏好推荐食谱的对话系统。用户可以通过两种不同的方式与Cora互动。他们可以通过点击按钮选择预定义的答案来与Cora交谈，或者用自然语言输入文本。此外，Cora可以通过社交对话与用户互动，也可以直入主题。Cora还能够提出不同的选择，并通过解释它们之间的权衡来为其食谱推荐提供理由。我们进行了两项实验。在第一项实验中，我们评估了Cora的对话技巧和用户的交互模式对用户对推荐食谱的看法和烹饪意愿的影响。我们的结果表明，一个通过建立融洽关系的对话来吸引用户的对话推荐系统可以提高用户对交互的看法以及对系统的看法。在第二项评估中，我们评估了Cora的解释和推荐比较对用户看法的影响。我们的结果表明，解释对用户对推荐系统的看法有积极影响。然而，将健康食谱与诱饵进行比较是一把双刃剑。虽然与单一的健康推荐相比，这种比较被认为明显更有用，但解释诱饵和健康食谱之间的差异实际上会使人们使用该系统的可能性降低。
Unhealthy eating behavior is a major public health issue with serious repercussions on an individual’s health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users’ needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users’ perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users’ eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora’s conversational skills and users’ interaction mode on users’ perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users’ perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora’s explanations and recommendation comparisons on users’ perception. Our results show that explanations positively influence users’ perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.
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