Multi-Armed Bandit API for Dynamic Webpage Selection
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Solution Overview
Problem
Existing online advertising systems face inefficiencies and inaccuracies in selecting optimal webpages for maximum revenue, due to manual adjustment processes and lengthy testing periods, which hinder real-time optimization and adaptation to user interactions, especially on mobile devices.
Innovation Solution
A system utilizing a self-serve tool integrated with a multi-armed bandit API that dynamically receives and analyzes creative variants, measures conversion rates, determines confidence intervals, and automatically selects and prioritizes the most profitable webpage for display, enabling real-time adjustments and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment processes are used to select optimal webpages, then flexibility in decision-making is maintained, but the time required for testing and implementation is significantly increased
Solution Approach 1:
The system enables self-service automated selection of optimal webpages through the multi-armed bandit API, which autonomously analyzes conversion rates and confidence intervals to determine winning variants without requiring manual intervention. This resolves the contradiction by replacing manual adjustment (ease of operation) with automated self-service processes that eliminate lengthy testing periods.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated computational system using the multi-armed bandit API. The API performs algorithmic exploration and exploitation phases, automatically adjusting webpage selection based on real-time performance data, thereby substituting human-operated mechanical processes with automated computational mechanisms that operate continuously without delay.
2Measurement precision
If traditional A/B testing is conducted to determine winning webpages, then comprehensive testing can be performed, but the process is lengthy and delays real-time optimization
Solution Approach 1:
The system implements dynamic webpage selection through the multi-armed bandit API, which continuously adapts its testing strategy based on real-time conversion rate data. Unlike static traditional A/B testing that requires fixed predetermined periods, the dynamic system adjusts exploration and exploitation phases autonomously, maintaining measurement precision while dramatically reducing testing duration by transitioning to production phase immediately upon identifying winning variants.
Solution Approach 2:
The system performs preliminary automated analysis of conversion rates and confidence intervals before committing to a winning webpage selection. The multi-armed bandit API conducts rapid algorithmic evaluation in advance, allowing the system to make informed decisions quickly and transition to production phase without lengthy testing periods, thus maintaining accuracy while reducing time investment.
3Adaptability or versatility
If multiple webpage variants are tested simultaneously, then the best performing page can be identified, but the complexity of managing and analyzing the variants increases
Solution Approach 1:
The multi-armed bandit API serves as an intermediary that manages the complexity of testing multiple webpage variants. The API autonomously handles the exploration and exploitation phases, automatically analyzing conversion rates and confidence intervals for all variants. This intermediary role resolves the contradiction by enabling versatile multi-variant testing while abstracting away the management complexity from the user.
Solution Approach 2:
The system implements continuous feedback loops where the multi-armed bandit API monitors conversion rates and confidence intervals for multiple variants in real-time. The feedback mechanism automatically adjusts the testing strategy, allocating traffic dynamically based on performance. This feedback-driven approach enables versatile multi-variant testing while reducing management complexity through automated real-time optimization.
4Productivity
If automated multi-armed bandit API is implemented, then real-time optimization is achieved, but the system complexity increases
Solution Approach 1:
The multi-armed bandit API performs multiple functions within a single unified system: it conducts algorithmic exploration, performs exploitation phase analysis, calculates conversion rates, determines confidence intervals, and automatically selects winning variants. This multi-functionality resolves the contradiction by achieving high-speed real-time optimization through a single versatile automated system rather than multiple separate complex components.
Data Source
AI summary
A system for online advertising. The system may include at least one memory unit for storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving a plurality of creatives for a webpage published to a viewer and resulting in a viewer experience; measuring, based on the viewer experience, a result including conversion rates for a plurality of variants of the webpage; determining confidence intervals in association with the conversion rates; dynamically comparing the received creatives, the conversion rates, and the determined confidence intervals of the variants; automatically analyzing, based on the comparison, the variants to select a winning webpage, the winning webpage exceeding a computed threshold; and automatically adjusting online traffic such that the selected winning webpage is displayed more frequently than other webpages.