Replacement Item Prediction Using Expiration-Aware ML Scoring
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Solution Overview
Problem
Current online concierge systems fail to consider expiration dates when suggesting replacement items, leading to unsuitable replacements and negative customer experiences, increased refund requests, and wasteful resource consumption.
Innovation Solution
The system incorporates expiration date normalization based on customer purchasing frequency and utilizes a machine learning model to calculate approval and similarity scores for candidate replacement items, determining an ensemble score that considers customer engagement and item similarity, thereby suggesting suitable replacements with appropriate expiration dates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system suggests replacement items based only on general suitability without considering expiration dates, then the system can quickly generate replacement suggestions, but the replacements may be unsuitable and lead to negative customer experiences
Solution Approach 1:
The system changes the parameter consideration from general item suitability to specific expiration date-based suitability. By normalizing expiration dates relative to the original item's expiration date and incorporating this normalized expiration factor into the machine learning model's feature set, the system achieves both efficient generation and reliable suitability assessment of replacement items.
2Measurement precision
If the system includes multiple datapoints including expiration dates in the analysis, then the accuracy of replacement identification improves, but the computing resource consumption increases
Solution Approach 1:
Instead of using raw expiration dates directly, the system transforms them into normalized expiration factors by dividing the difference in expiration dates by the original item's expiration date. This normalization reduces the magnitude of the data and allows the machine learning model to process expiration information more efficiently, improving accuracy while controlling computational resource usage.
3Adaptability or versatility
If the system uses machine learning models with multiple features including normalized expiration factors, then the personalization of replacement suggestions improves, but the model complexity increases
Solution Approach 1:
The system transforms expiration date information into a normalized factor that captures relative expiration timing. This single normalized feature integrates multiple aspects of expiration information (absolute dates, time differences, relative positioning) into one processed value, enabling personalization without proportionally increasing model complexity.
Data Source
AI summary
A specific item is identified to suggest a replacement therefor to a user. A set of candidate replacement items for the specific item is determined. For at least one of the candidate replacement items, an expiration score is determined based on expiration information associated with the item. A replacement score for the candidate replacement item is determined by inputting the determined expiration score as a feature into a machine learning model that is trained using features of historical samples of candidate replacement items suggested as a replacement to users and the replacement suggestion being accepted by the users. One or more of the candidate replacement items is selected based on respective replacement scores as one or more suggested replacement items. A graphical user interface of a client device of the user is caused to display the one or more suggested replacement items as the replacement for the specific item.