Demographic Classification Using Combined Algorithms
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Solution Overview
Problem
Conventional methods for demographic content placement are inaccurate and inefficient due to incomplete or inaccurate user profiles, leading to irrelevant digital content being shown to users, and existing classification algorithms fail to accurately identify target demographics, resulting in wasted advertising efforts.
Innovation Solution
A system and method that uses a unique combination of classification algorithms to determine demographic labels for users by analyzing digital inputs, including demographic-label statistics and session-level data, to enhance accuracy in predicting user demographics and personalize content placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification algorithms are used to classify users by demographic characteristics, then content placement can be performed, but the classification accuracy is low leading to incorrect user identification
Solution Approach 1:
The patent combines multiple classification algorithms (behavioral targeting algorithm, demographic algorithm, and machine learning algorithm) into an integrated system. The behavioral algorithm identifies users based on digital inputs, the demographic algorithm processes available demographic data, and the machine learning algorithm continuously improves accuracy by learning from validation feedback. This merging of algorithms resolves the contradiction by achieving high classification accuracy while maintaining effective content placement.
Solution Approach 2:
The system implements a feedback mechanism where a validation module tests the accuracy of classified target audiences using available demographic information. The results of this validation are fed back to improve the classification algorithms, creating a continuous improvement loop. This feedback process directly addresses the accuracy problem by enabling the system to learn from its mistakes and improve demographic classification over time.
2Reliability
If demographic information is collected from user profiles, then content targeting can be performed, but user profiles are often incomplete, inaccurate, or unavailable due to privacy concerns
Solution Approach 1:
The patent uses digital inputs (behavioral data, device information, interaction patterns) as an intermediary to infer demographic characteristics without directly collecting sensitive personal information. Instead of relying on user-provided profile data, the system analyzes behavioral patterns and digital footprints to derive demographic insights, thereby maintaining reliability while reducing complexity of direct data collection.
Solution Approach 2:
The system replaces the mechanical approach of directly collecting and storing demographic data from user profiles with an automated computational approach using machine learning algorithms. These algorithms process available digital inputs and infer demographic characteristics, eliminating the need for complete user profiles while maintaining accurate targeting capability.
3Productivity
If conventional content placement methods are used, then digital content can be delivered to users, but the content often reaches unintended demographic groups resulting in wasted advertising efforts
Solution Approach 1:
The system performs preliminary classification and validation of target audiences before content placement. The validation module tests the accuracy of the classified audience using available demographic information, and only after this preliminary validation does the system proceed with content delivery. This preliminary action ensures high measurement precision while maintaining productivity by avoiding wasted content delivery to incorrect audiences.
Data Source
AI summary
This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.