AI Entity Detection With Frequency Capping for Cross-Device Ad Serving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing advertising systems struggle to optimize and personalize advertisements across a diverse range of connected devices, leading to user experience degradation due to suboptimal and fractured ad consumption.
Innovation Solution
A system for programmatic generation of training data and entity detection using AI models, combined with advanced frequency management to optimize ad delivery, including logo overlay on media items, entity detection, and frequency threshold enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of Internet-connected devices increases, then the inventory of advertising platforms increases, but the user experience degrades due to ad fatigue and lack of optimization
Solution Approach 1:
The system changes parameters by implementing frequency capping mechanisms that limit the number of times a specific ad is shown to a user within a defined period. This parameter control transforms the uncontrolled quantity of ad exposures into a managed variable, reducing ad fatigue while maintaining effective advertising delivery across the expanded inventory of connected devices
Solution Approach 2:
The system employs feedback mechanisms through user behavior tracking and ad performance monitoring. By analyzing user interactions and ad effectiveness data, the system dynamically adjusts advertising delivery parameters to optimize user experience while maintaining campaign effectiveness across the growing inventory of Internet-connected devices
2Adaptability or versatility
If advertising platforms are integrated across the technology stack, then the availability of advertising inventory increases, but the complexity of managing and optimizing ads increases
Solution Approach 1:
The system segments the complex advertising management task into distinct functional modules: frequency capping service, ad selection service, user profile service, and performance tracking components. This segmentation allows each module to handle specific aspects of ad management independently, reducing overall system complexity while enabling comprehensive integration across multiple advertising platforms
Solution Approach 2:
The system implements universal services that can operate across multiple advertising platforms and device types simultaneously. The frequency capping service, for example, serves multiple functions by controlling ad exposure across different platforms while maintaining consistent user experience, thereby managing complexity through multi-functional design
3Object-affected harmful factors
If frequency capping is implemented to reduce ad fatigue, then user experience improves, but the quantity of ad impressions delivered decreases
Solution Approach 1:
The system applies local quality by implementing differentiated frequency caps for different ad categories, users, and contexts. Rather than applying a uniform limit across all advertising, the system adjusts impression thresholds based on local characteristics such as ad relevance, user preferences, and campaign objectives, thereby maintaining user experience while preserving adequate ad delivery volume
Data Source
AI summary
Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.