ML Basket Prediction Using Segmented Models
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Solution Overview
Problem
Existing systems face challenges in accurately predicting the size and quantity of items users will reorder in upcoming transactions, due to varying factors such as recent transaction history, purchasing frequency, and transient habits.
Innovation Solution
A machine learning architecture is employed to generate feature vectors based on historical data, using models like gradient boosted regression and random forest decision trees to predict basket size and item quantities, thereby populating a predicted basket with personalized item selections and quantities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used to forecast basket size and item quantities, then the system is simple to implement, but prediction accuracy deteriorates due to inability to capture complex user behavior patterns
Solution Approach 1:
The prediction system is segmented into multiple specialized models: a basket composition model that predicts the number of unique items, and quantity prediction models for each item category. This segmentation allows each model to focus on specific aspects of user behavior, improving overall prediction accuracy while managing complexity through modular design
Solution Approach 2:
Feature vectors serve as intermediaries between raw user data and prediction models. The system extracts meaningful features from user profiles, transaction history, and item characteristics, transforming complex raw data into structured representations that the prediction models can effectively process, thereby bridging the gap between data complexity and model simplicity
2Productivity
If the system attempts to predict all items and quantities for every user, then comprehensive coverage is achieved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The system applies different prediction strategies to different user segments and item categories based on their specific characteristics. High-value users receive more comprehensive predictions while lower-value users receive streamlined predictions. Similarly, different quantity prediction models are applied to different item categories (e.g., groceries vs. electronics), optimizing computational resources by matching prediction complexity to actual user needs and item importance
Solution Approach 2:
The system performs predictions selectively rather than universally. It focuses computational effort on predicting items and quantities that are most likely to be purchased based on user behavior patterns, omitting predictions for items with very low probability. This partial action approach maintains high accuracy for relevant predictions while reducing overall computational burden
3Adaptability or versatility
If the system uses detailed user transaction history and behavior data, then prediction personalization improves, but data privacy concerns and security risks increase
Solution Approach 1:
The system extracts only the essential features needed for prediction from complete user data, storing and processing minimal necessary information. Instead of retaining full transaction histories, it extracts aggregated statistics and behavioral patterns. This extraction approach enables personalization through feature vectors while minimizing exposure of sensitive user data, thereby reducing security risks and privacy concerns
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.