Multi-User Content Targeting via Characteristic Aggregation
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Solution Overview
Problem
Conventional targeted content delivery systems face challenges in effectively identifying and delivering content to multi-user interfaces, where multiple users with different characteristics are associated with the same device, leading to inconsistent and conflicting user data, which complicates the selection of relevant content.
Innovation Solution
The system identifies users and obtains their characteristics through a characteristic selection system, using methodologies such as relative priority ranking, interaction attribution, and common characteristic aggregation to determine a set of user characteristics for targeted content delivery, allowing for intelligent content selection and delivery to multi-user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional targeted content delivery systems deliver content to a device associated with multiple users, then the content can reach a broader audience, but the user characteristics become inconsistent and conflicting, reducing content relevance
Solution Approach 1:
The system segments multiple users into distinct user profiles with individual characteristics. Each user is identified and assigned separate characteristics, allowing the system to handle multi-user scenarios by dividing the aggregate user data into discrete, manageable segments that can be individually evaluated for content targeting.
Solution Approach 2:
The system introduces an intermediary characteristic selection system that acts as a mediator between the content delivery system and the multi-user interface. This intermediary resolves conflicts by selecting or aggregating characteristics from multiple users according to predefined rules, providing a unified set of characteristics to the content delivery system without requiring changes to either end.
2Quantity of substance
If the system identifies and uses characteristics from all users of a multi-user interface, then more comprehensive user data is available, but the complexity of determining which characteristics to utilize increases
Solution Approach 1:
The system implements dynamic characteristic selection where the set of user characteristics used for content delivery is not fixed but adapts based on the current context, user interactions, and predefined rules. The characteristic selection system can dynamically determine which users' characteristics to prioritize or aggregate based on real-time conditions, making the complex process flexible and adaptive rather than static and rigid.
Solution Approach 2:
The system changes parameters of user characteristics through aggregation and selection processes. Individual user characteristics are transformed into a unified set of characteristics that represent the multi-user interface. This parameter transformation includes weighting, averaging, or selecting dominant characteristics from multiple users, effectively changing the parameter space to resolve conflicts while maintaining comprehensive data utilization.
3Reliability
If targeted content is delivered based on individual user characteristics, then content relevance to each user is improved, but the ability to deliver consistent content to a shared device is reduced
Solution Approach 1:
The system merges individual user characteristics into a unified characteristic set for content delivery decisions. By combining characteristics from multiple users through aggregation rules (such as selecting common characteristics, weighting by user priority, or choosing characteristics from the most active user), the system achieves both relevance to individual users and consistency across the shared device experience.
Solution Approach 2:
The characteristic selection system serves multiple functions: it identifies individual users, aggregates their characteristics, resolves conflicts, and provides a unified characteristic set for content delivery. This multi-functional approach allows the same system component to handle both personalized relevance and device-level consistency, eliminating the need for separate mechanisms for each function.
Data Source
AI summary
The effectiveness of targeted content delivery at a multi-user interface can be directly linked to a proper targeting of users. A way of improving targeted content delivery at a multi-user interface can be to determine which users should be targeted based on one or more criteria. The present technology provides various methodologies for selecting one or more users associated with a multi-user interface to receive targeted content. Such users can be selected based on criteria associated with a ranking or priority of the users, criteria associated with an analysis of their interactions with the multi-user interface, criteria based on their most common characteristics, or any combination thereof. The user characteristics associated with such identified used can then be utilized to determine which content should be delivered to the multi-user interface.