Joint Prediction Model for Demographics via Feature Merging
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Solution Overview
Problem
Traditional methods for predicting user demographics and interests are inefficient and do not consider the interplay among data from different sources, making it challenging to accurately target content to users.
Innovation Solution
A joint prediction model is trained using data from multiple sources to simultaneously predict multiple pieces of demographic and interest information, utilizing a joint feature vector constructed from diverse data types, which is then used for targeted content distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional individual prediction engines are used to estimate demographic information from different sources, then each piece of demographic information can be predicted separately, but the system requires much resource, operates inefficiently, and does not consider the interplay among data
Solution Approach 1:
The patent combines multiple individual prediction engines into a single joint prediction model that simultaneously predicts multiple demographic attributes (age, gender, income, education) from multiple data sources. This merging eliminates the need for separate prediction operations for each demographic attribute, thereby improving operational efficiency while maintaining prediction accuracy through integrated learning of interplay among different data types.
Solution Approach 2:
The joint prediction model serves multiple functions by simultaneously predicting various demographic attributes (age group, gender, income bracket, education level) from a single integrated model. This multi-functional approach replaces the need for multiple specialized prediction engines, reducing system complexity and improving operational efficiency while considering the interplay among different data sources.
2Adaptability or versatility
If multiple separate prediction engines are deployed to predict different demographic information, then comprehensive demographic coverage is achieved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The patent merges multiple prediction engines into a single joint prediction model that handles multiple demographic attributes simultaneously. The model takes diverse input features (user profile data, device information, behavioral data) and predicts multiple demographic characteristics in one operation, thereby reducing system complexity while maintaining comprehensive demographic prediction coverage.
Solution Approach 2:
The joint prediction model is designed as a universal system that can predict various demographic attributes (age, gender, income, education) from a single model structure. This multi-functional design eliminates the need for multiple specialized engines, reducing device complexity while achieving comprehensive demographic coverage through integrated feature processing and prediction.
3Ease of manufacture
If traditional individual prediction methods are used, then implementation is simpler for each individual prediction, but the overall system does not consider the interplay among data from different sources
Solution Approach 1:
The patent combines multiple data sources and prediction tasks into a single joint prediction model that learns the interplay among different data types (user profile, device information, behavioral data). While the model structure is more complex than individual predictions, the integrated approach significantly improves prediction accuracy by capturing interactions among features that separate models would miss.
Solution Approach 2:
The joint prediction model uses a composite feature structure that integrates multiple data types (categorical features like device type, numerical features like usage frequency, hierarchical features like app categories) into a unified prediction framework. This composite approach allows the model to consider interplay among different data sources while maintaining a manageable implementation structure through standardized feature processing pipelines.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for joint prediction. Training data is obtained with information about a plurality of users collected from different sources and ground truth demographics/interests associated with each of the plurality users. Based on the training data, a joint prediction model is trained for simultaneously predicting multiple pieces of demographic/interest information. When information about a user from different sources is received, a joint feature vector is derived therefrom, which is then used by the trained joint prediction model to predict multiple pieces of demographic/interest information about the user.