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1865422

全球人工智慧驅動的食譜個人化平台市場：預測至2032年－按類型、部署方式、技術、最終用戶和地區分類的分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，全球人工智慧驅動的食譜個人化平台市場預計在 2025 年達到 29 億美元，預計到 2032 年將達到 51 億美元，在預測期內的複合年成長率為 8.3%。

人工智慧食譜個人化平台是一項數位服務，它利用機器學習演算法生成或修改食譜，使其精準匹配用戶的飲食需求、健康目標、口味偏好、烹飪技能水平以及可用食材。該平台整合了來自健身追蹤器、健康記錄和用戶輸入的數據，以最佳化營養成分和風味。該平台旨在簡化膳食計劃，促進更健康的飲食習慣，減少食物廢棄物，並提供高度個人化的烹飪體驗。

根據 Virtue Market Research 的說法，人工智慧食譜生成工具利用自然語言處理 (NLP) 和機器學習，根據食材、飲食限制和口味偏好來客製化膳食，從而提供便利性並改善健康益處。

消費者對客製化餐飲體驗的需求日益成長

在人們對個人化食物選擇日益成長的需求推動下，人工智慧驅動的食譜個人化平台正在改變消費者規劃和準備膳食的方式。人們對營養多樣性、飲食限制和口味偏好的日益關注，推動了各個年齡層對這類平台的接受度。先進的人工智慧演算法能夠根據卡路里需求、過敏史和文化偏好來客製化食譜。此外，社群媒體上推廣獨特用餐體驗的趨勢也提升了用戶參與度。在不斷變化的數位化生活方式的驅動下，個人化烹飪解決方案正在提高用戶滿意度。因此，個人化仍然是市場成長的核心驅動力。

食品和營養資料庫之間資料互通性的局限性

全球食品和營養體系缺乏標準化的數據框架，阻礙了人工智慧驅動的食譜推薦的準確性。成分標籤不一致、計量單位存在區域差異以及營養資料集不完整，都限制了模型的準確性。即使採用先進的演算法，資料碎片化也會降低平台的擴充性和互通性。此外，與第三方API的整合挑戰也阻礙了開發效率。資料孤島會導致使用者在食譜產生過程中遇到不一致的情況。因此，數據缺乏統一性仍然是限制人工智慧廣泛應用的主要市場障礙。

與語音助理和智慧廚房電器整合

在物聯網生態系統不斷發展的推動下，基於人工智慧的食譜平台正加速與智慧家居設備的整合，以實現無需手動操作的烹飪指導。 Alexa 和 Google Home 等語音助理提升了易用性，簡化了即時烹飪操作，而智慧烤箱、攪拌機和營養秤則能夠精準執行人工智慧生成的食譜。這種互聯互通的環境提升了便利性和使用者參與度。隨著連網家庭的興起，跨裝置同步功能能夠帶來身臨其境型的烹飪體驗。因此，智慧整合為市場擴張提供了巨大的成長機會。

演算法偏差導致食譜結果不一致

人工智慧訓練資料集中的偏差會導致食譜推薦不準確或帶有文化偏見。過度依賴有限的資料來源可能會忽略區域烹飪多樣性和食材供應。這些不一致性會削弱用戶信任，並降低個人化推薦的準確性。此外，有偏見的演算法可能會錯誤地呈現營養價值和膳食適宜性。人工智慧模型設計缺乏透明度導致監管審查日益嚴格。因此，演算法公平性和數據多樣性對平台信任和消費者接受度構成了重大威脅。

新冠疫情的感染疾病：

疫情改變了消費者的行為，加速了居家烹飪趨勢和數位化食譜的使用。在封鎖期間，人們尋求健康且經濟實惠的膳食解決方案，人工智慧膳食計劃工具也因此廣泛應用。同時，供應鏈中斷導致用戶依賴自適應食譜平台進行食材替換。受遠距辦公生活方式的影響，烹飪已成為注重健康的活動。即使在疫情結束後，人們對居家飲食和營養管理的持續關注仍然推動著市場擴張。因此，新冠疫情成為了數位化烹飪創新的一大催化劑。

預計在預測期內，健康和膳食計劃領域將佔據最大的市場佔有率。

在消費者對膳食健康促進和預防性營養日益成長的興趣推動下，健康膳食計劃領域預計將在預測期內佔據最大的市場佔有率。隨著對適合糖尿病患者、低碳水化合物和高蛋白食譜的需求不斷成長，各平台正在加強健康個人化服務。人工智慧系統分析生物標記和膳食目標，從而提供適應性提案。透過與營養師和健康應用程式的合作，建議的準確性得到了進一步提高。在全球健康趨勢的推動下，健康個人化正在推動該領域在市場價值方面佔據主導地位。

預計在預測期內，B2B餐飲整合細分市場將呈現最高的複合年成長率。

受餐飲服務業快速數位轉型的推動，B2B餐飲整合領域預計將在預測期內達到最高成長率。餐廳正採用人工智慧驅動的菜餚個人化客製化技術，為每位顧客提供專屬菜單並最佳化食材採購。隨著顧客對體驗式餐飲的期望日益提高，人工智慧的整合將有助於菜單創新和減少廢棄物。此外，與外送平台的合作將強化價值鏈。因此，數據驅動的食品客製化和營運效率的提升將有助於該領域的擴張。

佔比最大的地區：

預計亞太地區將在預測期內佔據最大的市場佔有率，這主要得益於智慧型手機普及率的提高以及中國、日本、印度和韓國等國數位化餐飲文化的蓬勃發展。受都市化和飲食習慣改變的推動，消費者越來越青睞人工智慧驅動的個人化餐食應用程式。亞太地區對智慧廚房技術的投資進一步推動了此類應用的普及，而可支配收入的成長和烹飪嘗試的活性化則加速了市場的成熟。在亞太地區創新生態系統的支持下，該地區將繼續引領全球市場。

年複合成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於其強大的技術基礎設施以及對人工智慧驅動型消費平台的早期應用。在物聯網廚房設備和人工智慧分析的深度整合推動下，消費者對高度個人化的烹飪體驗的需求日益成長。主要企業正大力投資機器學習模型，以提升口味預測和膳食搭配的精準度。此外，與餐廳和零售品牌的合作也進一步豐富了產品生態系統。因此，北美正在崛起為成長最快的創新中心。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

第5章 全球人工智慧驅動的食譜個人化平台市場（按類型分類）

  • 介紹
  • 健康膳食計劃
  • 過敏原和膳食替代
  • 口味和偏好個性化
  • 家庭和批量烹飪餐食的調整
  • 購物清單與購物整合
  • 餐廳菜單個性化

第6章 全球人工智慧賦能的食譜個人化平台市場（依部署方式分類）

  • 介紹
  • 行動應用整合
  • API/SDK 許可
  • 家用電器嵌入式軟體
  • 雲端基礎的SaaS平台
  • B2B 餐廳整合
  • 白牌解決方案

7. 全球人工智慧食譜個人化平台市場（按技術分類）

  • 介紹
  • 機器學習和建議引擎
  • 自然語言處理（NLP）
  • 電腦視覺
  • 預測營養演算法
  • 食品物聯網與家用電器整合

8. 全球人工智慧驅動的食譜個人化平台市場（按最終用戶分類）

  • 介紹
  • 個人消費者
  • 健康與營養應用程式
  • 食材自煮包配送服務商
  • 餐廳/雲廚房
  • 食品零售商
  • 醫療和營養服務

9. 全球人工智慧食譜個人化平台市場（按地區分類）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • 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.