Title: A Survey on Food Ingredient Substitutions Show Chinese title

Title: 食品配料替代综述

Abstract: Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to support ingredient substitution research; (ii) techniques and approaches applied to solve substitution problems (iii) contextual information of ingredients considered, such as nutritional content, flavor, and pairing potential; (iv) applications for which substitution models have been developed, including dietary restrictions, constraints, and missing ingredients; (v) safety and transparency of substitution models, focusing on user trust and health concerns. The survey also highlights promising directions for future research, such as integrating neuro-symbolic techniques for deep learning and utilizing knowledge graphs for improved reasoning, aiming to guide advancements in food computation and ingredient substitution. Show Chinese abstract

Abstract: 饮食在管理慢性疾病和整体健康中起着关键作用。 随着人们对食物选择变得更加挑剔，找到符合饮食需求的食谱变得很重要。 原料替换是适应饮食限制、过敏和可用性约束的关键。 然而，确定合适的替换物具有挑战性，因为它需要分析原料的风味、功能性和健康适宜性。 随着人工智能的进步，研究人员已经探索了计算方法来解决原料替换问题。 这篇综述论文全面概述了这一领域的研究，重点包括五个关键方面：(i) 用于支持原料替换研究的数据集和数据来源；(ii) 用于解决替换问题的技术和方法；(iii) 考虑到的原料上下文信息，如营养成分、风味和搭配潜力；(iv) 替换模型已开发的应用，包括饮食限制、约束和缺失原料；(v) 替换模型的安全性和透明度，重点关注用户信任和健康问题。 该综述还强调了未来研究的有前景方向，例如将神经符号技术集成到深度学习中，并利用知识图谱进行改进推理，旨在指导食品计算和原料替换的进步。