Contrast-Aware Ad Selection From Surrounding Social Media Posts
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Solution Overview
Problem
Current social media algorithms fail to ensure high or low contrast between advertisements and surrounding media, leading to less effective advertising and a negative user experience, as they do not consider visual features or higher-order features like format and tone when selecting advertisements.
Innovation Solution
Techniques that analyze features of preceding and subsequent posts, such as format, dominant color, and tone, to select advertisements with desired contrast, considering user and content provider preferences, and adjust content to achieve high or low contrast based on specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertisements are selected without considering contrast to surrounding media, then the advertising system is simple and fast, but advertisement effectiveness deteriorates and user experience is negatively impacted
Solution Approach 1:
The system performs preliminary analysis of surrounding media items to extract visual features (color, composition, format) and tone characteristics before advertisement selection. This pre-computation of contrast metrics enables efficient advertisement matching without real-time complex processing during user interaction.
Solution Approach 2:
The patent introduces an intermediary contrast analysis layer that mediates between the advertisement selection system and surrounding media. This intermediary layer computes visual and tonal contrast metrics as a bridge, enabling informed advertisement selection without directly complicating the core selection algorithm.
2Ease of operation
If advertisements blend in with surrounding media to avoid standing out, then user experience is improved, but advertisement effectiveness deteriorates
Solution Approach 1:
The system dynamically adjusts advertisement contrast based on surrounding media characteristics and user preferences. It can adaptively switch between high-contrast and low-contrast advertisement strategies, allowing the same advertisement to blend in when appropriate while standing out when needed, thus optimizing both user experience and effectiveness.
Solution Approach 2:
The patent changes key parameters of advertisement selection by introducing contrast as a selectable dimension. Advertisers can specify desired contrast levels (high or low) as a parameter, and the system adjusts advertisement selection accordingly, enabling flexible optimization of both visibility and user experience based on campaign goals.
3Productivity
If advertisements stand out with high contrast to ensure visibility, then advertisement effectiveness is improved, but user experience deteriorates due to distraction
Solution Approach 1:
The system applies different contrast strategies to different parts of the media feed. It analyzes local characteristics of surrounding media items and applies high contrast only where necessary (e.g., when surrounded by similar content) while using low contrast in other contexts, creating a non-uniform but optimized advertisement placement strategy.
4Measurement precision
If current advertising algorithms are used without contrast consideration, then implementation is simple, but visual feature analysis and higher-order feature consideration are lacking
Solution Approach 1:
The patent segments the analysis task into distinct modules: visual feature extraction (color, composition, format) and tone analysis. This segmentation allows precise measurement of individual features while keeping each module manageable and independent, reducing overall system complexity through modular architecture.
Data Source
AI summary
Systems and methods are provided herein for determining and selecting content based on associated media items. The disclosed systems and methods analyze media items (e.g., media posts) and determine whether content items (e.g., advertisements) with high contrast or low contrast should be selected to be displayed. For example, a content provider (e.g., an advertiser or advertising campaign manager) indicates that it prefers its content items to exhibit highly contrasting features relative to nearby media items. In such embodiments, the disclosed systems and methods analyze features of the nearby media items and select a content item that contrasts with those features. Conversely, if the content provider indicates that it prefers its content items to blend in with nearby media items, the disclosed systems and methods select a content item that exhibits low contrast with respect to those features.