Real-Time Bidding Ad Placement Scoring via Predictive Feedback
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Solution Overview
Problem
In real-time bidding for online advertisement placements, existing methods rely heavily on human judgment and are prone to errors, leading to inefficient bidding strategies that result in overpayment or missed valuable opportunities due to the difficulty in processing large-scale models within the short response time required for bid decisions.
Innovation Solution
A computer-implemented method and system that scores advertisement placements based on estimated feedback parameters, calculated from observed performance and similarity measures, to automatically optimize bidding and maximize campaign goals at market-efficient prices, using techniques such as collaborative filtering and probabilistic modeling to select optimal placements and adjust pricing dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional manual methods with static rules are used for advertisement bidding, then the process is simple to implement, but the bidding accuracy and campaign performance are poor due to human errors and inability to process large data sets
Solution Approach 1:
The system enables automated bid decision-making using machine learning models that independently analyze performance data and generate bidding recommendations without requiring manual intervention. The model automatically processes performance data from multiple sources, calculates predicted performance scores, and generates bidding recommendations, eliminating the need for manual data processing and decision-making while significantly improving bidding accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of reviewing spreadsheets and making bidding decisions with an automated machine learning system. The ML model processes performance data, calculates predicted performance scores, and generates bidding recommendations automatically, substituting human judgment and manual operations with computational algorithms that can process large data sets efficiently and accurately
2Measurement precision
If large scale models are used to predict advertisement performance, then the bidding accuracy improves, but the system cannot respond within the short time window of real-time bidding
Solution Approach 1:
The system performs preliminary analysis by pre-calculating performance predictions and identifying high-value bidding opportunities before the real-time bidding window opens. The ML model processes performance data and generates predicted performance scores in advance, allowing the system to quickly retrieve and act on pre-computed recommendations during the actual bidding event, thus maintaining both accuracy and speed
Solution Approach 2:
The patent segments the bidding process into distinct phases: data collection and model training occur over extended periods offline, while real-time bidding execution happens in the 50-100ms window. The system divides complex performance prediction into manageable components that can be pre-processed and stored, enabling rapid retrieval and decision-making during the actual bidding event without requiring complete model re-execution
3Ease of operation
If single static price bidding is used for advertisement placements, then the bidding process is simple, but advertisers pay too much or miss valuable bids due to inability to dynamically adjust prices
Solution Approach 1:
The system transitions from static single-price bidding to dynamic multi-price bidding by generating multiple bid price recommendations for each advertisement placement opportunity. The ML model considers various pricing scenarios and recommends optimal bid amounts based on predicted performance, placement value, and campaign budget constraints, enabling advertisers to dynamically adjust prices in real-time based on actual performance data and market conditions
Solution Approach 2:
The patent changes the bidding parameter from a single static price to multiple dynamic price points. The system calculates predicted performance scores and generates bidding recommendations with associated price adjustments based on performance metrics, placement quality, and campaign objectives. This allows advertisers to optimize their bid prices dynamically rather than using fixed prices, improving campaign efficiency and return on investment
Data Source
AI summary
A method and system for recommending advertisement placements based on scoring is disclosed. According to one embodiment, a computer-implemented method comprises receiving a real-time bidding (RTB) request for placing an online advertisement campaign. For each of a plurality of advertisement placements, a performance score is determined based on an estimated feedback parameter. The estimated feedback parameter is calculated from observed performance of the online advertisement campaign and similarity measures of other online advertisement campaigns. A first advertisement placement having a higher performance score is given more weight than a second advertisement placement having a lower performance score. A set of advertisement placements having their performance scores equal to or greater than the target rating is selected from the plurality of advertisement placements and provided for advertisement placements.