Ad Campaign Scoring With Content Standards Blocking
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Solution Overview
Problem
Existing advertising systems struggle to effectively manage and optimize the presentation of advertising campaigns across various content media to ensure compliance with global content standards and advertiser preferences, leading to inefficient and potentially harmful or inappropriate content associations.
Innovation Solution
A method involving training global standards models and campaign characteristic models to identify and block inappropriate content, and updating advertising scores based on category scores and advertiser preferences, ensuring that advertising campaigns are only presented with compliant and commercially advantageous content media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertising campaigns are presented across various content media to maximize reach and effectiveness, then advertising performance is improved, but the risk of associating ads with inappropriate or harmful content increases
Solution Approach 1:
The system performs preliminary classification of content media into categories and pre-establishes block lists for inappropriate categories before advertising campaigns are deployed. This advance preparation allows the system to quickly match ads with appropriate content while blocking harmful associations, resolving the contradiction between maximizing advertising reach and preventing inappropriate content pairing.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between advertising campaigns and content media. By categorizing content and using these categories as an intermediate layer, the system can efficiently determine appropriate ad-content pairings without direct exposure to all content, thus maintaining high advertising performance while filtering out harmful associations.
2Reliability
If manual review processes are used to ensure content compliance and appropriateness, then content quality is improved, but processing time and operational complexity increase
Solution Approach 1:
The system replaces manual review processes with automated machine learning models that classify content media into categories and generate block lists. This substitution maintains high content compliance reliability through consistent application of classification rules while dramatically reducing processing time by eliminating human review bottlenecks.
Solution Approach 2:
The classification system operates autonomously to categorize content media and generate block lists without requiring manual intervention. The models self-adjust and refine classifications based on training data, enabling the system to maintain reliable content compliance independently while processing large volumes of content rapidly.
3Object-affected harmful factors
If comprehensive content classification and blocking mechanisms are implemented to prevent inappropriate content presentation, then content safety is improved, but system complexity increases
Solution Approach 1:
The system segments the content moderation function into distinct modular components: content classification models that categorize media, block list generation mechanisms that define inappropriate categories, and ad selection logic that applies these block lists. This segmentation maintains content safety while managing complexity by creating independent, reusable modules that can be developed and maintained separately.
Solution Approach 2:
The classification models and block lists serve multiple functions simultaneously: they categorize content for safety purposes, enable efficient filtering of inappropriate material, and provide the basis for selecting appropriate advertising campaigns. This multi-functionality reduces overall system complexity by having single components perform multiple tasks rather than requiring separate systems for each function.
Data Source
AI summary
For communicating advertising campaigns, a method trains a global standards model with global content standards and/or advertising scores. The method trains a plurality of campaign characteristic models on campaign characteristics and/or the advertising scores. The method identifies whether content media violates the global content standards using the global standards model. In response to the content media violating the global content standards, the method adds the identified content media to a block list. The method updates the advertising score for the content media for each of the plurality of advertising campaigns based on the category scores and advertiser preferences for the plurality of advertising campaigns.