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市场调查报告书
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1865422

全球人工智慧驱动的食谱个人化平台市场：预测至2032年－按类型、部署方式、技术、最终用户和地区分類的分析

AI-Based Recipe Personalization Platforms Market Forecasts to 2032 - Global Analysis By Type, Deployment, Technology, End User and By Geography.
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，全球人工智慧驱动的食谱个人化平台市场预计在 2025 年达到 29 亿美元，预计到 2032 年将达到 51 亿美元，在预测期内的复合年增长率为 8.3%。

人工智慧食谱个人化平台是一项数位服务，它利用机器学习演算法生成或修改食谱，使其精准匹配用户的饮食需求、健康目标、口味偏好、烹饪技能水平以及可用食材。该平台整合了来自健身追踪器、健康记录和用户输入的数据，以优化营养成分和风味。该平台旨在简化膳食计划，促进更健康的饮食习惯，减少食物废弃物，并提供高度个人化的烹饪体验。

根据 Virtue Market Research 的说法，人工智慧食谱生成工具利用自然语言处理 (NLP) 和机器学习，根据食材、饮食限制和口味偏好来客製化膳食，从而提供便利性并改善健康益处。

消费者对客製化餐饮体验的需求日益增长

在人们对个人化食物选择日益增长的需求推动下，人工智慧驱动的食谱个人化平台正在改变消费者规划和准备膳食的方式。人们对营养多样性、饮食限制和口味偏好的日益关注，推动了各个年龄层对这类平台的接受度。先进的人工智慧演算法能够根据卡路里需求、过敏史和文化偏好来客製化食谱。此外，社群媒体上推广独特用餐体验的趋势也提升了用户参与度。在不断变化的数位化生活方式的驱动下，个人化烹饪解决方案正在提高用户满意度。因此，个人化仍然是市场成长的核心驱动力。

食品和营养资料库之间资料互通性的局限性

全球食品和营养体系缺乏标准化的数据框架，阻碍了人工智慧驱动的食谱推荐的准确性。成分标籤不一致、计量单位存在区域差异以及营养资料集不完整，都限制了模型的准确性。即使采用先进的演算法，资料碎片化也会降低平台的扩充性和互通性。此外，与第三方API的整合挑战也阻碍了开发效率。资料孤岛会导致使用者在食谱产生过程中遇到不一致的情况。因此，数据缺乏统一性仍然是限制人工智慧广泛应用的主要市场障碍。

与语音助理和智慧厨房电器集成

在物联网生态系统不断发展的推动下，基于人工智慧的食谱平台正加速与智慧家居设备的集成，以实现无需手动操作的烹饪指导。 Alexa 和 Google Home 等语音助理提升了易用性，简化了即时烹饪操作，而智慧烤箱、搅拌机和营养秤则能够精准执行人工智慧生成的食谱。这种互联互通的环境提升了便利性和使用者参与度。随着连网家庭的兴起，跨装置同步功能能够带来身临其境型的烹饪体验。因此，智慧集成为市场扩张提供了巨大的成长机会。

演算法偏差导致食谱结果不一致

人工智慧训练资料集中的偏差会导致食谱推荐不准确或带有文化偏见。过度依赖有限的资料来源可能会忽略区域烹饪多样性和食材供应。这些不一致性会削弱用户信任，并降低个人化推荐的准确性。此外，有偏见的演算法可能会错误地呈现营养价值和膳食适宜性。人工智慧模型设计缺乏透明度导致监管审查日益严格。因此，演算法公平性和数据多样性对平台信任和消费者接受度构成了重大威胁。

新冠疫情的感染疾病：

疫情改变了消费者的行为，加速了居家烹饪趋势和数位化食谱的使用。在封锁期间，人们寻求健康且经济实惠的膳食解决方案，人工智慧膳食计划工具也因此广泛应用。同时，供应链中断导致用户依赖自适应食谱平台进行食材替换。受远距办公生活方式的影响，烹饪已成为注重健康的活动。即使在疫情结束后，人们对居家饮食和营养管理的持续关注仍然推动着市场扩张。因此，新冠疫情成为了数位化烹饪创新的一大催化剂。

预计在预测期内，健康和膳食计划领域将占据最大的市场份额。

在消费者对膳食健康促进和预防性营养日益增长的兴趣推动下，健康膳食计划领域预计将在预测期内占据最大的市场份额。随着对适合糖尿病患者、低碳水化合物和高蛋白食谱的需求不断增长，各平台正在加强健康个人化服务。人工智慧系统分析生物标记和膳食目标，从而提供适应性提案。透过与营养师和健康应用程式的合作，建议的准确性得到了进一步提高。在全球健康趋势的推动下，健康个人化正在推动该领域在市场价值方面占据主导地位。

预计在预测期内，B2B餐饮整合细分市场将呈现最高的复合年增长率。

受餐饮服务业快速数位转型的推动，B2B餐饮整合领域预计将在预测期内达到最高成长率。餐厅正采用人工智慧驱动的菜餚个人化客製化技术，为每位顾客提供专属菜单并优化食材采购。随着顾客对体验式餐饮的期望日益提高，人工智慧的整合将有助于菜单创新和减少废弃物。此外，与外送平台的合作将强化价值链。因此，数据驱动的食品客製化和营运效率的提升将有助于该领域的扩张。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场份额，这主要得益于智慧型手机普及率的提高以及中国、日本、印度和韩国等国数位化餐饮文化的蓬勃发展。受都市化和饮食习惯改变的推动，消费者越来越青睐人工智慧驱动的个人化餐食应用程式。亚太地区对智慧厨房技术的投资进一步推动了此类应用的普及，而可支配收入的成长和烹饪尝试的活性化则加速了市场的成熟。在亚太地区创新生态系统的支持下，该地区将继续引领全球市场。

年复合成长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这主要得益于其强大的技术基础设施以及对人工智慧驱动型消费平台的早期应用。在物联网厨房设备和人工智慧分析的深度整合推动下，消费者对高度个人化的烹饪体验的需求日益增长。主要企业正大力投资机器学习模型，以提升口味预测和膳食搭配的精准度。此外，与餐厅和零售品牌的合作也进一步丰富了产品生态系统。因此，北美正在崛起为成长最快的创新中心。

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目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

第五章 全球人工智慧驱动的食谱个人化平台市场（按类型划分）

  • 介绍
  • 健康膳食计划
  • 过敏原和膳食替代
  • 口味和偏好个性化
  • 家庭和批量烹饪餐食的调整
  • 购物清单与购物整合
  • 餐厅菜单个性化

第六章 全球人工智慧赋能的食谱个人化平台市场（依部署方式划分）

  • 介绍
  • 行动应用整合
  • API/SDK 许可
  • 家用电器嵌入式软体
  • 云端基础的SaaS平台
  • B2B 餐厅整合
  • 白牌解决方案

7. 全球人工智慧食谱个人化平台市场（按技术划分）

  • 介绍
  • 机器学习和建议引擎
  • 自然语言处理（NLP）
  • 电脑视觉
  • 预测营养演算法
  • 食品物联网与家用电器集成

8. 全球人工智慧驱动的食谱个人化平台市场（按最终用户划分）

  • 介绍
  • 个人消费者
  • 健康与营养应用程式
  • 食材自煮包配送服务商
  • 餐厅/云厨房
  • 食品零售商
  • 医疗和营养服务

9. 全球人工智慧食谱个人化平台市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • Whisk
  • Yummly
  • SideChef
  • Innit Inc.
  • Edamam LLC
  • Spoonacular
  • Cookpad Inc.
  • Tasty
  • Foodvisor
  • Nutrino Health Ltd.
  • EatLove
  • Noom Inc.
  • PlateJoy
  • Bitesnap
  • Mealime
  • KitchenPal
  • FitMenCook
Product Code: SMRC32154

According to Stratistics MRC, the Global AI-Based Recipe Personalization Platforms Market is accounted for $2.9 billion in 2025 and is expected to reach $5.1 billion by 2032 growing at a CAGR of 8.3% during the forecast period. AI-Based Recipe Personalization Platforms are digital services that use machine learning algorithms to generate or modify recipes to precisely match an individual user's dietary needs, health goals, taste preferences, cooking skill level, and available ingredients. They integrate data from fitness trackers, health records, and user input to optimize nutritional content and flavor. The platform's purpose is to simplify meal planning, encourage healthier eating habits, reduce food waste, and provide a highly tailored culinary experience.

According to Virtue Market Research, AI recipe generators use NLP and machine learning to tailor meals based on ingredients, dietary restrictions, and taste preferences, enhancing convenience and health outcomes.

Market Dynamics:

Driver:

Rising consumer demand for customized meal experiences

Fueled by the growing desire for individualized culinary choices, AI-based recipe personalization platforms are transforming how consumers plan and prepare meals. Increasing awareness of nutritional diversity, dietary restrictions, and flavor preferences drives adoption across demographics. Enhanced AI algorithms now tailor recipes based on calorie needs, allergies, and cultural tastes. Moreover, social media trends promoting unique meal experiences amplify engagement. Spurred by digital lifestyle shifts, personalized cooking solutions enhance user satisfaction. Consequently, customization remains a core driver of market growth.

Restraint:

Limited data interoperability across food and nutrition databases

The lack of standardized data frameworks across global food and nutrition systems hampers the accuracy of AI-driven recipe recommendations. Inconsistent ingredient labeling, regional measurement variations, and incomplete nutrition datasets limit model precision. Even with advanced algorithms, fragmented data reduces platform scalability and interoperability. Additionally, integration challenges with third-party APIs slow development efficiency. Spurred by data silos, users may experience inconsistencies in recipe generation. Hence, limited data harmonization remains a key market restraint hindering widespread adoption.

Opportunity:

Integration with voice assistants and smart kitchen appliances

Propelled by the expanding IoT ecosystem, AI-based recipe platforms are increasingly integrating with smart home devices for hands-free culinary guidance. Voice-enabled assistants like Alexa and Google Home enhance accessibility, simplifying real-time cooking interactions. Meanwhile, smart ovens, mixers, and nutrition scales allow precise execution of AI-generated recipes. This interconnected environment promotes convenience and user engagement. Fueled by the rise of connected homes, cross-device synchronization enables immersive cooking experiences. Therefore, smart integration offers vast growth opportunities for market expansion.

Threat:

Algorithmic bias leading to inconsistent recipe outcomes

Bias within AI training datasets can cause inaccurate or culturally skewed recipe recommendations. Over-reliance on limited data sources may overlook regional cuisine diversity and ingredient availability. Such inconsistencies erode user trust and diminish personalization accuracy. Moreover, biased algorithms can misrepresent nutritional values or dietary suitability. Spurred by lack of transparency in AI model design, regulatory scrutiny is increasing. Consequently, algorithmic fairness and data diversity have become critical threats to platform reliability and consumer adoption.

Covid-19 Impact:

The pandemic reshaped consumer behavior, accelerating home cooking trends and digital recipe engagement. Lockdowns prompted widespread use of AI-based meal planning tools as households sought healthier, cost-efficient dining solutions. Simultaneously, supply chain disruptions led users to depend on adaptive recipe platforms for ingredient substitutions. Fueled by remote lifestyle patterns, cooking became a wellness-oriented activity. Post-pandemic, sustained interest in home dining and nutrition tracking continues to drive market expansion. Thus, COVID-19 acted as a major catalyst for digital culinary innovation.

The health-targeted meal plans segment is expected to be the largest during the forecast period

The health-targeted meal plans segment is expected to account for the largest market share during the forecast period, resulting from rising consumer focus on dietary wellness and preventive nutrition. Fueled by growing demand for diabetes-friendly, low-carb, and protein-optimized recipes, platforms increasingly emphasize health-driven personalization. AI systems analyze biomarkers and dietary goals to deliver adaptive recommendations. Moreover, collaborations with nutritionists and wellness apps enhance precision. Spurred by global wellness trends, health-targeted personalization drives the segment's dominance in market value.

The b2b restaurant integrations segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the B2B restaurant integrations segment is predicted to witness the highest growth rate, propelled by rapid digital transformation in the food service industry. Restaurants are adopting AI recipe personalization to deliver unique, customer-specific menus and optimize ingredient sourcing. Fueled by rising expectations for experiential dining, AI integration supports menu innovation and waste reduction. Additionally, partnerships with food delivery platforms strengthen value chains. Hence, the segment's expansion is reinforced by data-driven culinary customization and operational efficiency.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to growing smartphone penetration and digital food culture across China, Japan, India, and South Korea. Spurred by urbanization and changing dietary lifestyles, consumers increasingly favor AI-enabled meal personalization apps. Regional investments in smart kitchen technology further boost adoption. Additionally, rising disposable incomes and culinary experimentation accelerate market maturity. Supported by local innovation ecosystems, Asia Pacific continues to lead global market dominance.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with its strong technological infrastructure and early adoption of AI-driven consumer platforms. Fueled by robust integration of IoT-enabled kitchen devices and AI analytics, users increasingly demand hyper-personalized cooking experiences. Major players invest heavily in machine learning models to enhance taste prediction and dietary alignment. Furthermore, partnerships with restaurants and retail brands enrich product ecosystems. Consequently, North America emerges as the fastest-growing innovation hub.

Key players in the market

Some of the key players in AI-Based Recipe Personalization Platforms Market include Whisk, Yummly, SideChef, Innit Inc., Edamam LLC, Spoonacular, Cookpad Inc., Tasty, Foodvisor, Nutrino Health Ltd., EatLove, Noom Inc., PlateJoy, Bitesnap, Mealime, KitchenPal, and FitMenCook.

Key Developments:

In May 2025, Edamam launched an upgraded version of its Nutrition Analysis API, improving accuracy in dietary tagging and allergen detection. The update supports real-time recipe personalization for food delivery and wellness platforms.

In April 2025, Innit expanded its Food Intelligence Platform to support over 2 million product scores and personalized nutrition insights. The update includes AI-driven grocery planning and automated cooking instructions tailored to health conditions and dietary goals.

In March 2025, Yummly enhanced its AI capabilities by launching Yummly Smart Meal Planner, which uses dietary goals, cooking time, and pantry items to generate weekly meal plans. It also added voice-controlled cooking instructions compatible with smart kitchen devices.

In January 2025, Cookpad expanded its global recipe-sharing community by launching localized AI-curated content in Southeast Asia. The platform now supports multilingual recipe generation and ingredient substitution based on regional availability.

Types Covered:

  • Health-Targeted Meal Plans
  • Allergen & Diet Restriction Substitution
  • Flavor & Preference Personalization
  • Family & Batch Meal Scaling
  • Grocery List & Shopping Integration
  • Restaurant Menu Personalization

Deployments Covered:

  • Mobile App Integration
  • API & SDK Licensing
  • Embedded Appliance Software
  • Cloud-Based SaaS Platforms
  • B2B Restaurant Integrations
  • White-Label Solutions

Technologies Covered:

  • Machine Learning & Recommendation Engines
  • Natural Language Processing (NLP)
  • Computer Vision
  • Predictive Nutrition Algorithms
  • Integration with Food IoT & Appliances

End Users Covered:

  • Individual Consumers
  • Health & Nutrition Apps
  • Meal Kit Providers
  • Restaurants & Cloud Kitchens
  • Grocery Retailers
  • Healthcare & Dietician Services

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Based Recipe Personalization Platforms Market, By Type

  • 5.1 Introduction
  • 5.2 Health-Targeted Meal Plans
  • 5.3 Allergen & Diet Restriction Substitution
  • 5.4 Flavor & Preference Personalization
  • 5.5 Family & Batch Meal Scaling
  • 5.6 Grocery List & Shopping Integration
  • 5.7 Restaurant Menu Personalization

6 Global AI-Based Recipe Personalization Platforms Market, By Deployment

  • 6.1 Introduction
  • 6.2 Mobile App Integration
  • 6.3 API & SDK Licensing
  • 6.4 Embedded Appliance Software
  • 6.5 Cloud-Based SaaS Platforms
  • 6.6 B2B Restaurant Integrations
  • 6.7 White-Label Solutions

7 Global AI-Based Recipe Personalization Platforms Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning & Recommendation Engines
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Computer Vision
  • 7.5 Predictive Nutrition Algorithms
  • 7.6 Integration with Food IoT & Appliances

8 Global AI-Based Recipe Personalization Platforms Market, By End User

  • 8.1 Introduction
  • 8.2 Individual Consumers
  • 8.3 Health & Nutrition Apps
  • 8.4 Meal Kit Providers
  • 8.5 Restaurants & Cloud Kitchens
  • 8.6 Grocery Retailers
  • 8.7 Healthcare & Dietician Services

9 Global AI-Based Recipe Personalization Platforms Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 Whisk
  • 11.2 Yummly
  • 11.3 SideChef
  • 11.4 Innit Inc.
  • 11.5 Edamam LLC
  • 11.6 Spoonacular
  • 11.7 Cookpad Inc.
  • 11.8 Tasty
  • 11.9 Foodvisor
  • 11.10 Nutrino Health Ltd.
  • 11.11 EatLove
  • 11.12 Noom Inc.
  • 11.13 PlateJoy
  • 11.14 Bitesnap
  • 11.15 Mealime
  • 11.16 KitchenPal
  • 11.17 FitMenCook

List of Tables

  • Table 1 Global AI-Based Recipe Personalization Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Based Recipe Personalization Platforms Market Outlook, By Type (2024-2032) ($MN)
  • Table 3 Global AI-Based Recipe Personalization Platforms Market Outlook, By Health-Targeted Meal Plans (2024-2032) ($MN)
  • Table 4 Global AI-Based Recipe Personalization Platforms Market Outlook, By Allergen & Diet Restriction Substitution (2024-2032) ($MN)
  • Table 5 Global AI-Based Recipe Personalization Platforms Market Outlook, By Flavor & Preference Personalization (2024-2032) ($MN)
  • Table 6 Global AI-Based Recipe Personalization Platforms Market Outlook, By Family & Batch Meal Scaling (2024-2032) ($MN)
  • Table 7 Global AI-Based Recipe Personalization Platforms Market Outlook, By Grocery List & Shopping Integration (2024-2032) ($MN)
  • Table 8 Global AI-Based Recipe Personalization Platforms Market Outlook, By Restaurant Menu Personalization (2024-2032) ($MN)
  • Table 9 Global AI-Based Recipe Personalization Platforms Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 10 Global AI-Based Recipe Personalization Platforms Market Outlook, By Mobile App Integration (2024-2032) ($MN)
  • Table 11 Global AI-Based Recipe Personalization Platforms Market Outlook, By API & SDK Licensing (2024-2032) ($MN)
  • Table 12 Global AI-Based Recipe Personalization Platforms Market Outlook, By Embedded Appliance Software (2024-2032) ($MN)
  • Table 13 Global AI-Based Recipe Personalization Platforms Market Outlook, By Cloud-Based SaaS Platforms (2024-2032) ($MN)
  • Table 14 Global AI-Based Recipe Personalization Platforms Market Outlook, By B2B Restaurant Integrations (2024-2032) ($MN)
  • Table 15 Global AI-Based Recipe Personalization Platforms Market Outlook, By White-Label Solutions (2024-2032) ($MN)
  • Table 16 Global AI-Based Recipe Personalization Platforms Market Outlook, By Technology (2024-2032) ($MN)
  • Table 17 Global AI-Based Recipe Personalization Platforms Market Outlook, By Machine Learning & Recommendation Engines (2024-2032) ($MN)
  • Table 18 Global AI-Based Recipe Personalization Platforms Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 19 Global AI-Based Recipe Personalization Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 20 Global AI-Based Recipe Personalization Platforms Market Outlook, By Predictive Nutrition Algorithms (2024-2032) ($MN)
  • Table 21 Global AI-Based Recipe Personalization Platforms Market Outlook, By Integration with Food IoT & Appliances (2024-2032) ($MN)
  • Table 22 Global AI-Based Recipe Personalization Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global AI-Based Recipe Personalization Platforms Market Outlook, By Individual Consumers (2024-2032) ($MN)
  • Table 24 Global AI-Based Recipe Personalization Platforms Market Outlook, By Health & Nutrition Apps (2024-2032) ($MN)
  • Table 25 Global AI-Based Recipe Personalization Platforms Market Outlook, By Meal Kit Providers (2024-2032) ($MN)
  • Table 26 Global AI-Based Recipe Personalization Platforms Market Outlook, By Restaurants & Cloud Kitchens (2024-2032) ($MN)
  • Table 27 Global AI-Based Recipe Personalization Platforms Market Outlook, By Grocery Retailers (2024-2032) ($MN)
  • Table 28 Global AI-Based Recipe Personalization Platforms Market Outlook, By Healthcare & Dietician Services (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.