Sponsored Company Postings via User Profile Matching
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Solution Overview
Problem
Existing advertising methods on social networks are inefficient as they often display company postings to users who are not interested or part of the target demographic, leading to wasted resources for companies and irrelevant content for users.
Innovation Solution
A system that uses user characteristics, such as social network profiles, behavior, and social graphs to selectively display sponsored company postings to users who are most likely to be interested, by analyzing and matching user profiles with company profiles to determine relevance and presenting messages based on aggregate company scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If company postings are displayed to all users on the platform, then advertising reach is maximized, but advertising efficiency deteriorates due to wasted resources on uninterested users
Solution Approach 1:
The patent applies local quality by tailoring the advertising content delivery to specific user segments based on their characteristics. Instead of uniform distribution to all users, the system analyzes user profiles, behaviors, and social graphs to identify and target specific demographics who are more likely to be interested in the company postings, thereby improving advertising efficiency while maintaining reasonable reach
Solution Approach 2:
The system changes the parameter of user selection from random or uniform distribution to targeted selection based on multiple parameters including user characteristics, social network profiles, behaviors, and social graphs. This parameter change enables the system to optimize the balance between reach and efficiency by adjusting which users receive which postings based on calculated relevance scores
2Measurement precision
If user profile analysis and matching is implemented, then advertising relevance is improved, but system complexity increases
Solution Approach 1:
The patent segments the user base into different groups based on their characteristics, behaviors, and social network attributes. By dividing users into segments and matching company postings to appropriate segments, the system achieves high advertising relevance without requiring complex individualized analysis for every user-posting pair. The segmentation approach simplifies the matching process while maintaining precision
Solution Approach 2:
The system performs preliminary action by pre-analyzing and storing user characteristics, social network profiles, and behavior patterns before the actual advertising delivery. User profiles are pre-processed and segmented in advance, creating a ready-to-use framework for rapid matching when company postings need to be delivered. This preliminary preparation reduces the complexity of real-time decision-making
Data Source
AI summary
A system may include a network interface, a user interface, and a recommendation engine. The user interface may be configured to receive a company characteristic of a company profile of a company posted to the social network and a company bid from an entity related to company to the social network. The recommendation engine may be configured to determine an aggregate company score for the user based on a relevance of the company characteristic to a user characteristic and the company bid. The network interface may be configured to transmit a message related to the company to the user based, at least in part, on the aggregate company score.