User Profile Completion via Concept Matching and Signature Analysis
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Solution Overview
Problem
Existing solutions for determining user preferences are often inaccurate and incomplete due to users providing only partial information due to privacy concerns, and require active input or lengthy data collection, leading to cumbersome user profiling for online advertisers.
Innovation Solution
A method and system that analyze user profiles to identify missing informational elements by matching concepts with category concepts, generating signatures, and updating profiles based on query responses, utilizing a Deep Content Classification system and Signature Generator System to enhance user profiling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If active input is requested from users to specify their interests, then user profile information can be obtained, but users tend to provide only their current interests which can change over time, resulting in inaccurate profiles
Solution Approach 1:
The system performs preliminary analysis of user-generated content (posts, comments, shares) to proactively identify user interests before users are prompted to provide information. This allows the system to pre-fill profile sections with inferred interests, reducing the need for users to repeatedly update their profiles as interests change over time.
Solution Approach 2:
The system continuously monitors user activity and provides feedback by updating profile information based on new content analysis. When users create new posts or engage with content, the system analyzes this feedback to dynamically update user interests, ensuring the profile remains accurate without requiring active user input.
2Reliability
If users are required to provide information regularly, then user profile can be updated, but it becomes cumbersome and irritating for users, resulting in decreased interest in responding
Solution Approach 1:
The system enables self-service by automatically analyzing user-generated content and updating profile information without requiring user action. Users simply need to create content naturally (posts, comments, shares), and the system autonomously extracts interests and updates the profile, eliminating the need for users to manually fill out forms or respond to prompts.
Solution Approach 2:
The system performs preliminary analysis of user content to pre-identify interests before users are asked to provide information. This reduces the frequency and burden of user input by anticipating what information will be needed based on existing content patterns.
3Loss of information
If passive tracking of user activity is performed, then user information can be collected over time, but the information revealed is typically limited due to privacy concerns, resulting in incomplete user profiles
Solution Approach 1:
The system extracts only the specific information elements needed for profiling (interests, preferences, topics) from user-generated content, rather than collecting and storing all user data. This selective extraction approach reduces privacy concerns while maintaining profile completeness, as only relevant informational elements are retained.
Solution Approach 2:
The system segments user information into distinct categories (interests, preferences, demographics) and collects only the necessary segments for each category. This modular approach to data collection reduces overall complexity and privacy intrusion while still building comprehensive profiles through targeted information gathering.
Data Source
AI summary
A system and method for at least partially completing a user profile. The method includes analyzing the user profile to identify at least one missing informational element in the user profile, wherein identifying the at least one missing element further comprises determining at least one concept based on the user profile and matching the determined at least one concept to a plurality of category concepts, each concept including a collection of signatures and metadata describing the concept, wherein each category concept is associated with at least one required informational element, wherein each missing informational element is one of the at least one required informational element that is not included in the user profile; sending a query for the missing informational element; and updating at least a portion of the user profile based on a response to the query.