Augmented Expert Hierarchy for User Segment Prediction
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Solution Overview
Problem
Existing machine learning systems face challenges in effectively integrating heterogeneous data sources and predicting user segments due to task heterogeneity, data long-tailness, and availability issues, which limits their ability to leverage relationships among experts and adapt to diverse online activities.
Innovation Solution
The implementation of an expert hierarchy with initial and augmented experts, where augmented experts are trained to enhance the knowledge of initial experts by incorporating their outputs, and a nonlinear framework for integrating expert outputs using an artificial neural network to capture complex relationships among experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear models are used to combine predictions from individual experts, then the integration process is simple and computationally efficient, but the system cannot capture complex inter-relationships among data and different data sources
Solution Approach 1:
The patent transitions from linear combination parameters to neural network parameters, allowing the system to learn complex nonlinear relationships among experts' predictions. The neural network introduces new parameters (weights and biases across multiple layers) that can capture intricate interactions while maintaining computational tractability through gradient-based optimization.
Solution Approach 2:
The patent replaces the mechanical linear combination system with a neural network-based system. Instead of using fixed linear equations to combine expert predictions, the system employs a learned neural network model that can adaptively capture complex relationships, substituting a rigid mechanical approach with a flexible learned approach.
2Adaptability or versatility
If multiple heterogeneous experts are integrated to improve prediction coverage, then the range of knowledge is expanded, but the complexity of the system increases
Solution Approach 1:
The patent creates a universal neural network framework that can integrate multiple heterogeneous experts with different knowledge domains. The neural network serves as a multi-functional integrator that processes predictions from various expert types (e.g., content-based, collaborative filtering, hybrid) through a unified architecture, allowing the system to handle diverse prediction tasks without requiring separate integration mechanisms for each expert type.
3Use of energy by moving object
If traditional linear integration methods are used, then the computational resources required are minimal, but the prediction accuracy is limited due to inability to model complex relationships
Solution Approach 1:
The patent introduces dynamic learning capabilities through the neural network, allowing the integration process to adaptively adjust its parameters based on the input data and expert predictions. Instead of static linear weights, the system dynamically learns optimal combination strategies through training, enabling it to capture complex relationships while managing computational resources efficiently through shared representations and gradient-based optimization.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for predicting user segment. An expert hierarchy is created with an initial expert layer with multiple initial experts and at least one augmented expert layer. Each augmented expert layer has one or more augmented experts that are derived via machine training to augment at least the initial experts. When an input is received by the expert hierarchy, each of the experts, including initial and augmented, generates an expert prediction based on the input.