Adaptive Online Model Updating for Advertisement Selection
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Solution Overview
Problem
Existing advertisement selection models struggle to adapt to temporal changes and environmental factors, such as seasonality and market trends, leading to sub-optimal performance over the model's life-cycle due to fixed hyper-parameters and the time-consuming nature of periodic grid-search updates.
Innovation Solution
An adaptive hyper-parameter tuning algorithm that continuously generates and evaluates multiple model variations using parallel processing, selecting the best performing configuration and updating the model in real-time to ensure optimal performance in response to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a fixed hyper-parameters set is used for the advertisement selection model, then the model structure remains stable and easy to manage, but the model performance deteriorates over time due to temporal changes and environmental factors
Solution Approach 1:
The patent implements dynamic hyper-parameter tuning by continuously adjusting model parameters based on real-time performance feedback. The system monitors model performance metrics and automatically updates hyper-parameters to adapt to changing environmental conditions, transforming the static model into a dynamic system that evolves with the data distribution changes.
Solution Approach 2:
The patent changes the parameter values of the advertisement selection model by performing hyper-parameter tuning. It systematically explores different parameter configurations and selects optimal values that maximize model performance under current conditions, allowing the model to adapt to temporal changes without structural modifications.
2Reliability
If parallel grid-search is performed periodically to update the model with fresh logged data, then the model performance is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary hyper-parameter tuning by pre-selecting a subset of promising parameter configurations before full model training. This preliminary filtering step reduces the search space and enables faster iteration, allowing the system to prepare multiple candidate models in advance rather than performing exhaustive grid-search from scratch each time.
Solution Approach 2:
The patent applies partial grid-search by focusing computational resources on the most promising hyper-parameter configurations identified through preliminary analysis. Instead of exhaustively searching all possible parameter combinations, it performs targeted tuning on selected candidates, reducing overall computational burden while maintaining effective model updates.
3Adaptability or versatility
If the model is updated frequently to adapt to temporal changes, then the model remains relevant and performant, but the system complexity and computational overhead increase
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors model performance metrics and uses this information to trigger hyper-parameter tuning only when performance degradation is detected. This feedback-driven approach enables adaptive updates based on actual need rather than fixed schedules, maintaining model relevance while avoiding unnecessary updates that would increase system complexity.
Solution Approach 2:
The patent enables the model to self-adjust its hyper-parameters through automated tuning processes that respond to performance feedback without requiring manual intervention. The system autonomously identifies when tuning is needed, selects appropriate parameter configurations, and applies updates, reducing operational complexity while maintaining high adaptability to changing conditions.
Data Source
AI summary
The present teaching relates to generating an updated model related to advertisement selection. In one example, a request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met.