Recipe Recommendation via Pantry Model and Ingredient Overlap
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Solution Overview
Problem
Users often end up improvising recipes or making additional purchases due to lack of suitable ingredients, as current systems fail to recommend recipes based on the ingredients they have available.
Innovation Solution
A recommendation system that infers the customer's available ingredients using a pantry model, selects candidate recipes based on ingredient overlap, and optimizes recommendations using a machine learning model to suggest recipes that can be made with the ingredients on hand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system recommends recipes without considering customer's available ingredients, then the recommendation process is simple, but the customer may need to make additional purchases or improvise recipes
Solution Approach 1:
The system performs preliminary analysis of the customer's pantry contents and ingredient availability before generating recipe recommendations. By pre-assessing what ingredients the customer has on hand and what will be available after their current shopping order, the system can filter and rank recipes accordingly, saving the customer time on additional purchases and recipe planning.
Solution Approach 2:
The system incorporates feedback from the customer's shopping cart and pantry data to continuously refine recipe recommendations. By monitoring what items the customer has already purchased and what they have stored in their pantry, the system adjusts its recommendations to ensure ingredient availability, preventing the need for additional trips to the store.
2Reliability
If the system analyzes customer pantry and shopping cart to recommend recipes, then recipe recommendations are more accurate, but the system complexity increases
Solution Approach 1:
The system segments the ingredient availability assessment into distinct components: analyzing current pantry contents separately from analyzing shopping cart items, then combining these assessments. This modular approach to evaluating ingredient availability makes the complex analysis manageable and allows each component to be optimized independently while maintaining overall recommendation accuracy.
Solution Approach 2:
The system introduces an intermediary layer that bridges the customer's existing ingredients and the recipe database. This intermediary component processes the intersection between available ingredients and recipe requirements, filtering recipes based on ingredient overlap. This mediator simplifies the complexity by creating a clear mapping between what the customer has and what recipes can be made.
3Adaptability or versatility
If the system recommends recipes requiring missing ingredients, then the customer can discover new recipes, but the customer needs to make additional purchases
Solution Approach 1:
The system applies partial action by recommending recipes that require only a subset of missing ingredients, prioritizing recipes where the customer already has most ingredients on hand. This approach balances recipe discovery with minimizing additional purchases, suggesting recipes that can be made with mostly existing ingredients and just one or two additional items from the shopping cart or future purchases.
Data Source
AI summary
An online recommendation system can choose recipes to recommend to a customer based on a set of ingredients the customer is inferred to have on hand (a customer pantry model). For example, the recommendation system can look at recent or historical purchases made by the customer and determine what items the customer still has available based on an assumed shelf life for the purchased items. Using the customer pantry model, the recommendation system selects recipes based on overlapping ingredients between recipe's ingredient lists and ingredients available to the customer (including the customer pantry model and their current shopping cart). In some implementations, the recommendation system first selects a set of candidate recipes based on the overlap, then selects the final set of recipes to recommend based on a score optimization (for example, performed using a machine learning model).