Suggested Shopping Lists Using ML Ranking and Collection Rules
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Solution Overview
Problem
Existing online systems for generating shopping lists are time-consuming and inconvenient for customers, leading to dissatisfaction and a preference for manual shopping, and there is a challenge in optimizing computing resource usage.
Innovation Solution
An online system generates a suggested shopping list by populating a template with item categories and quantities using a machine learning model trained on user historical data, applying conversion likelihood predictions and ranking item types based on historical order information, and updating the list dynamically based on collection rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If customers manually create shopping lists and provide collection instructions, then the system can fulfill specific customer requirements, but the process becomes time-consuming and inconvenient for customers
Solution Approach 1:
The system automatically generates shopping lists by accessing machine learning models that predict conversion likelihoods for item categories based on historical order information. The system self-services the shopping list creation process without requiring manual customer input, thereby eliminating the time loss and inconvenience associated with manual list creation while still fulfilling customer requirements through automated prediction algorithms
2Ease of operation
If the system automatically generates shopping lists using machine learning models, then customer convenience is improved, but computing resources such as processing power and network bandwidth are consumed
Solution Approach 1:
The machine learning models are pre-trained using historical order information stored in data stores, so that when shopping list generation is needed, the system can quickly query pre-computed predictions rather than performing complex real-time calculations. This preliminary action reduces the computing resources required during actual shopping list generation while maintaining high customer convenience
3Reliability
If customers receive damaged or spoiled items, then the system must process replacements and refunds, but this increases the complexity of order fulfillment
Solution Approach 1:
The system uses feedback from collection rules and item quality checks to automatically update shopping lists. When damaged or spoiled items are detected, the system receives feedback about the issue and automatically modifies the shopping list to include replacement items, thereby maintaining reliability without significantly increasing operational complexity through automated feedback loops
Data Source
AI summary
An online system generates a template shopping list for a user by accessing a machine learning model trained based on historical order information associated with the user, applying the model to predict likelihoods of conversion for item categories by the user, and populating the template shopping list with one or more item categories based on the predicted likelihoods. The system ranks one or more item types associated with each item category in the template shopping list and determines a set of collection rules associated with one or more item categories/types based on the historical order information. The system generates a suggested shopping list by populating each item category in the template shopping list with one or more item types and a quantity of each item type based on the ranking and rules and sends the suggested shopping list and rules for display to a client device associated with the user.