Recipe and Dish Recommendation

Learn more about how to integrate personalized recommendations with user data in the Nutritional Plans, Daily Nutritional Goal and Food Restrictions and Preferences sections.

Plan & Token Requirements

Feature available in the following LogMeal Plans:

Accessible by the following User Types:

What It Does

Recipe Recommendation uses LogMeal’s AI to generate personalized recipe suggestions for each user based on their nutritional preferences, restrictions, intakes, and goals. It combines the user’s dietary data (e.g., intakes history, calorie targets, preferred cuisines, allergens) with nutrient balance models and recipe databases to return suitable options.

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When to Use It / Outcomes

• You want to recommend recipes that align with a user’s nutritional goals.
• You need to generate meal ideas based on available ingredients or pantry items.
• You want to automate meal planning for users based on calorie and macro targets.
• You wish to personalize content within a wellness app, fitness coach platform, or nutrition tool.

Feature-Specific Details

• Recommendation engine: Combines AI models trained on user intake history, nutritional plans, and food preferences.
• Filters and constraints: recipes can be filtered by allergens, ingredients, diet types, cuisine, or preparation time.
• Personalization parameters: user data (age, gender, goals, activity level) and past behavior influence results.
• Integration: works seamlessly with Remaining Daily Intake, Food Restrictions, and Intakes History.
• Localization: recipe titles and ingredient names adapt to user language preferences.

Related Endpoints

Use the following endpoints to generate and personalize recommendations:

• GET /recommend/dish → 🔴 Get dish recommendations for an 🔴 APIUser based on profile and history.
• GET /recommend/recipe → 🔴 Get recipe recommendations for an 🔴 APIUser with optional filters (diet, allergens, ingredients).
• POST /profile/modifyUserProfileInfo → ⚫ 🔴 🔵 Modify the user profile information to personalize recognition and recommendations.

Remember to check applicable request limitations inside each of the endpoints.

Typical Workflow

1. Collect user data: Ensure profile info (preferences, restrictions, goals) is up to date via POST /profile/modifyUserProfileInfo. Make sure the user has submitted some intakes into their profile.
2. Request recommendations: Call GET /recommend/dish or GET /recommend/recipe to obtain AI-generated suggestions.
3. Display results: Show dish/recipe candidates with nutrition info and match scores; allow users to filter and favorite.