Machine-Learning Credit Allocation for Expiration-Aware Orders
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Solution Overview
Problem
Users face difficulties in efficiently managing and allocating credits from multiple programs with varying expiration dates when placing orders, leading to potential expiration of unused credits and increased interaction requirements with online systems.
Innovation Solution
An online system uses a machine-learning model to predict credit expirations and automatically allocate default credits based on expiration rankings, updating the user interface to minimize user interactions for optimal credit selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually manage and allocate credits from multiple programs, then they can control which credits to use, but the complexity of managing multiple credits with different expirations increases significantly
Solution Approach 1:
The system automatically performs credit allocation without requiring user intervention. The online system retrieves credit information, predicts expirations using machine learning, ranks credits by urgency, and applies them to orders automatically, allowing the system to serve itself rather than requiring manual user management
Solution Approach 2:
The system performs preliminary actions by pre-calculating credit expirations, pre-ranking credits by expiration urgency, and pre-selecting optimal credits before the user places an order. This preliminary processing eliminates the need for users to manually manage credit allocation at the time of ordering
2Productivity
If users manually allocate credits to orders, then they can optimize credit usage, but the time and interactions required to complete the allocation increase
Solution Approach 1:
The online system automatically completes the entire credit allocation process without requiring user interactions. The system retrieves credit information, predicts expirations, ranks credits, and applies them to orders automatically, eliminating the time users would otherwise spend on manual allocation
Solution Approach 2:
The system performs all credit allocation decisions in advance before the user finalizes their order. By pre-calculating which credits to use based on predicted expirations and item eligibility, the system eliminates time-consuming user interactions during the ordering process
3Ease of operation
If users forget to use credits before expiration, then they can simplify the ordering process, but credits expire unused causing loss of value
Solution Approach 1:
The system continuously monitors credit expirations and provides feedback to the user interface about which credits will expire soonest. This feedback mechanism ensures users are aware of impending credit expirations without complicating the ordering process, enabling timely usage while maintaining simplicity
Solution Approach 2:
The system predicts credit expirations in advance using machine learning models and proactively alerts users before credits expire. By performing this prediction and notification in advance, the system prevents credit waste without requiring users to actively track expiration dates themselves
Data Source
AI summary
An online system displays an ordering interface and, responsive to receiving a request from a client device associated with a user to place an order, retrieves information describing a set of unused credits provided to the user by each of one or more programs. The system identifies a set of the program(s), wherein the set of unused credits provided by each identified program is eligible to be used for acquiring an item in the order. The system accesses and applies a machine-learning model to predict an expiration of the set of unused credits provided to the user by each identified program based on the retrieved information and a current time. The system ranks the set of programs based on the prediction(s), determines a default allocation of a subset of each set of unused credits to the order based on the ranking, and updates the interface to include the default allocation.