ML Cost Estimation for Content Delivery Bidding
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Solution Overview
Problem
Current methods for estimating the initial cost of recommended content items in content delivery platforms are inaccurate, leading to inefficient delivery and poor budget utilization, especially for content with short-term timeliness, as they rely on predetermined formulas that do not account for the unique characteristics of individual content items.
Innovation Solution
A method using a trained machine learning model to extract feature information from related data and determine a personalized initial cost for each recommended content item, improving the accuracy of initial bid estimation and enhancing delivery efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predetermined formulas are used to estimate initial cost, then the estimation process is simple, but the accuracy of cost estimation deteriorates
Solution Approach 1:
The patent transforms the cost estimation approach from using fixed predetermined formulas to using dynamic machine learning models that process multiple feature parameters (content characteristics, historical delivery data, resource attributes) to generate accurate initial cost estimates, thereby resolving the contradiction between simplicity and accuracy
Solution Approach 2:
The patent replaces the mechanical calculation system (predetermined formulas) with an intelligent system (machine learning cost estimation model) that automatically learns optimal cost parameters from historical data, achieving both accuracy and operational simplicity through automation
2Productivity
If personalized initial cost estimation is implemented for each content item, then the accuracy and delivery efficiency improve, but the system complexity increases
Solution Approach 1:
The patent creates a universal cost estimation system that handles diverse content items through a single machine learning model framework, which automatically adapts to different content types and scenarios, achieving personalized estimation without proportionally increasing system complexity
Solution Approach 2:
The cost estimation model performs self-learning and self-optimization through automated training on historical delivery data, reducing the need for manual system configuration and maintenance, thereby improving delivery efficiency while keeping operational complexity manageable
3Reliability
If accurate initial cost estimation is achieved through machine learning models, then budget utilization improves, but the model training and deployment time increases
Solution Approach 1:
The patent performs preliminary model training using historical delivery data before actual content delivery operations, so that when the system needs to estimate costs for new content items, the model is already trained and ready to provide accurate estimates immediately, thus improving budget utilization without adding time loss during operational phases
Solution Approach 2:
The patent optimizes model parameters and architecture to achieve faster inference times while maintaining high accuracy, and uses efficient training techniques to reduce model training time, thereby resolving the contradiction between reliability and time loss
Data Source
AI summary
According to embodiments of the present disclosure, method, apparatus, device, and storage medium for delivery cost estimation are provided. The method comprises: extracting first feature information specific to a target recommended content item from related data of the target recommended content item, and the target recommended content item is to be delivered to recommend target resources; at least based on the first feature information, using a trained cost estimation model, determining an expected initial cost in a contending delivery of the target recommended content item; and determining a target initial cost used in the contending delivery of the target recommended content item based on the expected initial cost. According to the solution, the initial cost of each recommended content item during contending delivery can be accurately estimated specifically to improve delivery performance.