Widget Placement via Machine Learning Contextual Estimation
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Solution Overview
Problem
Existing search engine systems face challenges in optimally placing new widgets on graphical user interfaces due to limited historical data, leading to irrelevant placements and slow learning processes, especially for large user bases, and rely on rule-based systems that require extensive keyword gathering and curating, resulting in biased data and long training times.
Innovation Solution
The implementation of machine learning-based contextual estimation using exploration-exploitation tradeoffs through multi-armed bandit models to determine ranking scores and uncertainties for widget placement, allowing the system to intelligently decide between exploring new options and exploiting known positives, thereby minimizing user experience harm and accelerating the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rule-based systems are used for widget placement, then placement decisions can be made with existing keywords, but the system requires extensive keyword gathering and curating, resulting in biased data and long training times
Solution Approach 1:
The patent replaces rule-based mechanical keyword matching systems with machine learning models that automatically learn placement patterns from data, eliminating the need for manual keyword gathering and curating while reducing training time through efficient learning algorithms
Solution Approach 2:
The system enables self-service learning where the machine learning model automatically acquires knowledge about widget placement from historical data without requiring manual keyword curation, allowing the system to improve its placement decisions autonomously over time
2Adaptability or versatility
If random placement is used for new widgets, then implementation can be done for large user bases, but the overall user experience is hurt as irrelevant widgets may be placed in unjustifiably high positions
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical widget placement data before deploying to production, so that when new widgets are placed for large user bases, the system already has learned patterns to make relevant placements rather than relying on random placement
Solution Approach 2:
The system implements feedback mechanisms where user interactions with widgets are continuously collected and used to retrain and improve the machine learning models, ensuring that placement relevance improves over time while maintaining scalability to large user bases
3Measurement precision
If machine learning models rely on historical information, then placement decisions can be data-driven, but there is little to no historical information available when a new widget is generated, resulting in slow learning processes
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical widget placement data before deploying to production, so that when new widgets are placed for large user bases, the system already has learned patterns to make relevant placements rather than relying on random placement
Solution Approach 2:
The system transitions from relying solely on historical widget data to incorporating contextual dimensions such as query information, user profiles, and real-time interactions, allowing the model to make accurate placement decisions for new widgets even with limited direct historical data by leveraging related contextual features
4Productivity
If exploration of new widget options is increased, then the learning process can be accelerated, but user experience harm increases as irrelevant widgets may be displayed
Solution Approach 1:
The patent applies partial action by implementing exploration only for a small portion of the user base rather than all users, allowing the system to gather learning data from exploratory placements while limiting the exposure of irrelevant widgets to the broader user population, thus balancing learning speed with user experience protection
Solution Approach 2:
The system dynamically adjusts the exploration-exploitation parameter based on confidence levels and performance metrics, increasing exploration when the model is uncertain and decreasing it when confidence is high, thereby optimizing both learning speed and user experience by adapting the level of exploration to current system knowledge
Data Source
AI summary
Methods, systems, and apparatuses include receiving query data. Contextual data is generated based on the received query data. Feature vectors are generated for a new widget based on the query data and contextual data. A widget ranking model is configured using the feature vectors. The configured widget ranking model is applied to a plurality of widgets. The new widget is assigned to a widget slot using an output of the configured widget ranking model. Reward data is received from a graphical user interface in response to presentation of the new widget in the assigned widget slot. The configured widget ranking model is updated based on the reward data.