Customized Content Generation via User Profile Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current content distribution systems fail to provide personalized and contextually relevant advertisements to users based on their specific interests and preferences, often relying on generic targeting methods that do not account for user-specific information such as age, location, or social connections.
Innovation Solution
The system generates customized content by receiving a search query from a user, identifying user-specific factors, and configuring advertising content based on this information, including demographics and social network data, to create tailored messages that are delivered at appropriate times and locations, such as during birthdays or alma mater events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic targeting methods are used for content distribution, then device complexity is reduced and ease of operation is improved, but ad relevance and user engagement deteriorate
Solution Approach 1:
The patent segments the advertising content delivery system into multiple components: a profile generation module that creates user profiles from social graph data, a content selection module that matches ads to user profiles, and a delivery module that presents personalized content. This segmentation enables sophisticated personalization while managing system complexity through modular design.
Solution Approach 2:
The patent introduces user profiles as an intermediary layer between the advertising system and social graph data. These profiles aggregate and structure information from multiple sources (demographics, interests, social connections), serving as a mediator that enables relevant ad targeting without requiring direct access to complex social graph structures.
2Adaptability or versatility
If user-specific information is collected and processed, then ad relevance and user engagement are improved, but loss of information and privacy concerns increase
Solution Approach 1:
The patent extracts only the necessary information elements from the social graph data to create user profiles, rather than collecting or storing entire social graphs. The system takes out specific attributes (demographics, interests, connection types) needed for ad personalization while leaving out sensitive personal information, thereby reducing privacy risks.
Solution Approach 2:
The patent applies different levels of information collection and processing to different aspects of user data. Sensitive personal information is handled with stricter privacy controls, while less sensitive attributes like demographic information and general interests are used more freely for personalization. This local quality approach balances personalization effectiveness with privacy protection.
Data Source
AI summary
Example techniques for generating customized content may include the following operations: receiving a search query from a computing device associated with a user; performing a search of electronic content based on the search query; obtaining a search result based on the search of electronic content; obtaining configurable content that relates to the search query, where the configurable content includes a field that is configurable; identifying user-specific content based on the search query; configuring the field of the configurable content based on the user-specific content to thereby produce configured content; and outputting data corresponding to the search result and the configured content for use in generating a Web page containing the search result and the configured content.