Ad Server Peer Rating Filtering for Mobile Ad Targeting
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Solution Overview
Problem
Existing mobile advertising methods fail to effectively target users with specific interests within the same demographic, resulting in low user responses and low return on investment (ROI) due to lack of user-specific information and peer feedback for ad ranking.
Innovation Solution
An ad server system that filters and prioritizes advertisements based on meta data, end user profiles, and peer ratings from multiple mobile devices, using a social ranking algorithm to enhance ad selection and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If demographic-based ad targeting is used, then ad delivery to large user groups is simplified, but user-specific relevance and response rate deteriorate
Solution Approach 1:
The patent segments users beyond demographics by introducing peer-based segmentation. Instead of treating all users in a demographic group uniformly, the system divides them into sub-groups based on their peer ratings and ad preferences. This allows the system to maintain demographic-level simplicity while adding peer-level precision to improve user response rates.
Solution Approach 2:
The patent implements feedback mechanisms where users rate advertisements and this feedback is aggregated to form peer ratings. These peer ratings are then fed back into the ad selection process to dynamically adjust which ads are displayed to which users. This closed-loop feedback system transforms static demographic targeting into dynamic, user-responsive targeting that continuously improves relevance and response rates.
2Measurement precision
If peer rating feedback mechanism is added, then user-specific ad selection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple data sources (demographic information, user ratings, peer ratings) into a unified ad selection model. By combining these data types in a coordinated manner, the system achieves high measurement precision for ad relevance without treating each data source as a separate complex subsystem. The merged model processes all inputs through a single decision framework, managing complexity while improving accuracy.
Solution Approach 2:
The patent uses peer ratings as copies or proxies for direct user preferences. Instead of requiring every user to provide explicit feedback on every ad, the system creates copies of preference information from peer users. These copied ratings serve as stand-ins for individual user preferences, enabling high-precision ad selection without the complexity of collecting and processing extensive direct user feedback data.
3Manufacturing precision
If user-specific ad filtering is implemented, then ad relevance to individual users is improved, but processing time and infrastructure requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing peer ratings and user preference profiles before ad delivery time. By preparing this information in advance and caching it, the system reduces the processing time required during actual ad selection. The infrastructure processes and stores preference data proactively, so that when ads need to be delivered, the system can quickly retrieve and apply pre-computed ratings without time-consuming real-time analysis.
Data Source
AI summary
For use in a wireless communication network, a method and system for targeting advertising for a mobile device is provided. The method includes receiving a plurality of advertisements from an advertising distributor. The method also includes filtering the advertisements based on meta data for each advertisement. The method further includes receiving ratings of the advertisements from a plurality of mobile devices. The method also includes further filtering the advertisements based on an end user profile and the ratings from the plurality of mobile devices. The method still further includes transmitting a selected advertisement to the first mobile device.