AI Recipe Tagging for Shopping List-Based Recommendations
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Solution Overview
Problem
Current online shopping platforms lack a comprehensive solution for assisting users in finding recipes based on the items in their shopping list, making it difficult for users to decide what to cook with the selected ingredients.
Innovation Solution
An online system uses artificial intelligence to identify key ingredients for recipes and recommends recipes based on a user's shopping list, utilizing a recipe database and machine learned models to tag key ingredients and rank recipes accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive recipe database is created with AI-tagged key ingredients, then recipe recommendation accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-processing recipes to identify and tag key ingredients using machine learned models before users search for recipes. This advance preparation creates a ready-to-query database structure that enables fast, accurate recipe recommendations without requiring complex real-time analysis when users interact with the system.
Solution Approach 2:
The patent introduces an intermediary layer (machine learned models) that automatically tags key ingredients in recipes, serving as a mediator between the raw recipe database and the user query system. This intermediary structure simplifies the overall system by providing a standardized interface for recipe matching without requiring users to manually analyze ingredient lists.
2Speed
If all recipes are pre-processed to identify key ingredients using AI models, then recipe matching speed is improved, but computational resources and time required for processing increase
Solution Approach 1:
The system performs preliminary action by pre-processing recipes to identify and tag key ingredients using machine learned models before users search for recipes. This advance preparation creates a ready-to-query database structure that enables fast, accurate recipe recommendations without requiring complex real-time analysis when users interact with the system.
Solution Approach 2:
The patent applies partial action by focusing the AI processing only on identifying key ingredients in recipes rather than analyzing all aspects of each recipe. This selective processing approach reduces the computational burden while still providing sufficient information for accurate recipe matching based on user shopping lists.
3Ease of operation
If the system provides detailed recipe information and recommendations, then user decision-making is improved, but information overload may occur
Solution Approach 1:
The system extracts and highlights only the most relevant information - specifically the key ingredients that match the user's shopping list - while omitting less relevant details. This selective presentation approach helps users make decisions about what to cook without being overwhelmed by complete recipe information, allowing them to focus on recipes that actually utilize their purchased items.
Data Source
AI summary
An online concierge system may update, responsive to a request from a user client device, a shopping list with a food item. The system may query a recipe database based in part on the food item to obtain one or more recipes that use the food item as a key ingredient, where the key ingredients in a recipe are tagged in the recipe database. The key ingredients for each of the corresponding recipes are identified using a machine learned model. The system ranks the one or more recipes based on one or more ranking criteria, such as a number of key ingredients of the recipe that are present in the shopping list. The system may provide the one or more ranked recipes to the user client device for presentation.