Customized GUI Generation Using Dynamic Predictive Algorithms
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Solution Overview
Problem
Machine learning algorithms face inaccuracies due to insufficient or non-representative training data, leading to issues in applications like automated trading and search engine marketing, as they can overpredict or underpredict outcomes, and struggle with new or unseen signals.
Innovation Solution
A system and method that determines similar and complementary items to an item of interest, applies labels based on their rank, trains a predictive algorithm, and generates a customized graphical user interface (GUI) using this algorithm to address the lack of training data by dynamically updating and utilizing current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more training data is gathered to improve machine learning model accuracy, then the model precision improves, but the time required for data collection increases
Solution Approach 1:
The system performs preliminary actions by gathering interaction data in real-time as users naturally interact with items on the platform. Instead of waiting to collect large datasets before training, the system continuously accumulates data from user behaviors (views, purchases, clicks) and immediately uses this data to train and update machine learning models, eliminating the need for separate data collection phases
Solution Approach 2:
The system serves itself by automatically generating training data from its own operational data. User interactions with items on the platform create natural training signals that the system captures and uses to train its own recommendation models, eliminating the need for external data sources or manual data gathering processes
2Measurement precision
If pre-trained models or purchased training data are used to improve model performance, then the model accuracy improves, but the cost increases
Solution Approach 1:
The system eliminates external dependencies by using its own operational data from user interactions to train its models. This self-service approach replaces expensive pre-trained models and purchased datasets with freely available data generated from platform users' natural behaviors, significantly reducing costs while maintaining model accuracy
Solution Approach 2:
Instead of purchasing expensive proprietary training datasets from vendors, the system creates its own training data by copying and utilizing the interaction patterns that naturally occur on its platform. This approach replicates the value of purchased data using internally generated information
3Measurement precision
If pre-trained models are used to improve model performance, then the model accuracy improves, but the adaptability to new signals decreases
Solution Approach 1:
The system implements dynamic model updating by continuously retraining models as new interaction data becomes available. Instead of relying on static pre-trained models, the system adapts its models in real-time to reflect changing user behaviors and emerging patterns, ensuring both accuracy and adaptability to new signals
Solution Approach 2:
The system establishes a feedback loop where user interactions continuously inform model updates. New signals and interaction patterns are immediately captured and fed back into the training process, allowing the model to adapt to emerging trends and new types of user behavior while maintaining accuracy on established patterns
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform functions comprising determining one or more similar items similar to an item; determining one or more complementary items complementary to both the one or more similar items and the item; applying one or more labels to the one or more complementary items based on a rank of the one or more complementary items; training a predictive algorithm on the one or more labels; receiving a request to generate a customized graphical user interface (GUI) for the item; and coordinating displaying the customized GUI for the item using the predictive algorithm. Other embodiments are disclosed herein.