Machine-Learned User Interface Customization Models
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Solution Overview
Problem
Traditional approaches to real-time content selection for user interfaces fail to account for the incremental utility of input elements, leading to inefficiencies and redundant rendering of elements that provide little to no incremental value, often requiring extensive manual analysis and hand-tuning of models.
Innovation Solution
A system that uses a first machine-learned model to predict the incremental value of rendering an input element, updating a second machine-learned model to optimize real-time content selection by generating semi-supervised training datasets and calibrating parameters for improved user interface customization, allowing for automated adaptation and efficient allocation of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional approaches are used for real-time content selection, then user interface elements are rendered without considering incremental utility, but this leads to duplication of elements and inefficient resource usage
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline to predict incremental utility of content elements before real-time rendering. The first model predicts baseline conversions and the second model predicts conversions with the element, allowing the system to pre-determine which elements provide incremental value without performing heavy computations at render time.
Solution Approach 2:
The system implements feedback mechanisms where user interaction data and conversion information are continuously fed back into the machine learning models to refine predictions. The models learn from actual user behavior patterns to improve their ability to predict incremental utility, creating a feedback loop that optimizes content selection over time.
2Measurement precision
If manual analysis and hand-tuning of models is performed, then model accuracy can be improved, but this increases time consumption and operational complexity
Solution Approach 1:
The system performs self-service by automatically training and optimizing machine learning models using available data without requiring extensive manual intervention. The automated pipeline includes data collection, model training, evaluation, and deployment, where the system monitors performance and retrain models as needed, eliminating the need for continuous manual tuning.
Solution Approach 2:
The system changes parameters by automatically adjusting model hyperparameters and selection criteria based on performance metrics. The machine learning framework automatically optimizes parameters such as learning rates, regularization strengths, and feature weights to maximize prediction accuracy, replacing manual parameter tuning with automated optimization algorithms.
3Reliability
If all input elements are rendered regardless of incremental value, then user interface completeness is maintained, but this causes redundant rendering of elements with little to no value
Solution Approach 1:
The system applies local quality by differentiating the treatment of different content elements based on their predicted incremental utility. Instead of uniformly rendering all elements, the system selectively renders only those elements with positive incremental value, applying quality differentiation at the element level while maintaining overall interface completeness through intelligent selection.
Solution Approach 2:
The system introduces intermediary machine learning models as mediators between content providers and the user interface rendering process. These models evaluate and rank content elements based on predicted incremental utility, acting as a filter that selects only valuable elements for rendering, thereby simplifying the content selection process while maintaining interface reliability.
Data Source
AI summary
Example embodiments of the present disclosure provide for an example method. The example method includes obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively including an interaction with an input element rendered at a user device and a request for a resource associated with the input element. The example method includes obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element. The example method includes updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements.