Neural Network Lift Prediction With Shared Control-Treatment Parameters
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Solution Overview
Problem
Conventional models for predicting incremental lift in online systems face challenges in processing large data volumes and produce biased results due to imbalanced treatment and control groups, making it difficult to accurately predict user actions on content items.
Innovation Solution
A machine-learned neural network architecture with shared and unshared parameter subsets is employed to generate control and treatment predictions, allowing for flexible modeling of user and item features, even with imbalanced training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional models are used to predict incremental lift, then the model structure is simple, but the model cannot accurately predict user actions due to limited data processing capacity and biased results from imbalanced treatment and control groups
Solution Approach 1:
The neural network model is segmented into multiple portions (first portion, second portion, third portion) that can be independently trained and optimized. Each portion processes different aspects of the prediction task, allowing the complex prediction problem to be broken down into manageable components while maintaining overall accuracy.
Solution Approach 2:
The neural network model serves multiple functions: it processes both treatment and control group data, handles imbalanced datasets, and generates incremental lift predictions. The shared architecture across different portions allows the model to be trained efficiently on limited data while maintaining predictive accuracy across different scenarios.
2Measurement precision
If a neural network model with multiple portions is used, then prediction accuracy improves, but the model complexity increases
Solution Approach 1:
The neural network model is divided into distinct portions (first portion for treatment predictions, second portion for control predictions, third portion for incremental lift calculations) that can be independently optimized. This segmentation allows each portion to specialize in specific aspects of the prediction task while sharing underlying features, reducing redundant complexity.
Solution Approach 2:
The model merges the treatment and control prediction capabilities into a unified neural network architecture with shared feature extraction layers. This combining approach allows the model to learn common patterns from both groups while maintaining separate prediction pathways, improving data utilization efficiency and reducing overall model complexity.
3Quantity of substance
If treatment and control groups are significantly different in size, then more data is available for the larger group, but the results become inaccurate and biased
Solution Approach 1:
The model applies different parameter configurations to handle imbalanced data sizes. The first portion of the neural network is trained with parameters optimized for the treatment group, while the second portion uses parameters optimized for the control group. This parameter differentiation allows each portion to adapt to its specific data characteristics, compensating for the imbalance and improving overall prediction accuracy.
Solution Approach 2:
The model performs preliminary data processing and feature extraction to create balanced representations of treatment and control groups before final prediction. By preprocessing the data to normalize group differences and extract invariant features, the model prepares the input data in a way that reduces the impact of group size imbalance on prediction accuracy.
Data Source
AI summary
An online system trains a machine-learned lift prediction model configured as a neural network. The machine-learned lift prediction model can be used during the inference process to determine lift predictions for users and items associated with the online system. By configuring the lift prediction model as a neural network, the lift prediction model can capture and process information from users and items in various formats and more flexibly model users and items compared to existing methods. Moreover, the lift prediction model includes at least a first portion for generating control predictions and a second portion for generating treatment predictions, where the first portion and the second portion share a subset of parameters. The shared subset of parameters can capture information important for generating both control and treatment predictions even when the training data for a control group of users might be significantly smaller than that of the treatment group.