Generative-Discriminative Ensemble for User Lifetime Value Prediction
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Solution Overview
Problem
Current methods for predicting user lifetime value rely on either generative or discriminative models, which have limitations such as rigid assumptions, inability to handle new features, and reliance on stale data, leading to inaccurate predictions.
Innovation Solution
An ensemble approach combining generative and discriminative models, with a meta-model to weigh their outputs and adjust feature importance, maximizing training data usage and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If generative models are used to predict lifetime value, then the amount of training data that can be utilized is maximized, but prediction accuracy for individual users deteriorates due to aggregate-level predictions
Solution Approach 1:
The patent combines generative models and discriminative models into an ensemble system. The generative model processes all available training data to capture aggregate patterns, while the discriminative model processes a holdout set to capture individual user patterns. Their predictions are merged through a meta-model that learns optimal weighting, thereby achieving both maximum data utilization and high individual prediction accuracy.
2Measurement precision
If discriminative models are used to predict lifetime value, then per-user prediction accuracy is improved, but the amount of training data that can be utilized deteriorates due to holdout period requirements
Solution Approach 1:
The patent segments the training data into two distinct portions: a primary training set used by the generative model and a holdout set used by the discriminative model. This segmentation allows each model to specialize - the generative model learns from the bulk of data for aggregate patterns, while the discriminative model learns from the holdout set for individual user patterns, thereby maximizing overall data utilization while maintaining high accuracy.
3Adaptability or versatility
If generative models are used to predict lifetime value, then flexibility in handling new features is improved, but ability to account for personalized user behaviors deteriorates
Solution Approach 1:
The patent introduces a meta-model as an intermediary that receives predictions from both the generative model (which handles new features flexibly) and the discriminative model (which captures personalized behaviors). The meta-model learns optimal weighting strategies to combine these predictions, thereby inheriting the flexibility of the generative model while incorporating the personalization capabilities of the discriminative model.
4Measurement precision
If an ensemble of generative and discriminative models is used, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The meta-model serves as an intermediary layer that simplifies the ensemble system by automatically learning optimal weighting strategies between the generative and discriminative models. This intermediary handles the complexity of coordinating multiple models, allowing the overall system to achieve high prediction accuracy while managing complexity through automated weight optimization rather than manual configuration.
Data Source
AI summary
The example embodiments are directed toward predicting the lifetime value of a user using an ensemble model. In an embodiment, a system is disclosed, including a generative model for generating a first prediction representing a first lifetime value of a user during a forecasting period and a discriminative model configured for generating a second prediction representing a second lifetime value of the user during the forecasting period. The system further includes a meta-model for receiving the first prediction and the second prediction and generating a third prediction based on the first prediction and the second prediction, the third prediction representing a third lifetime value of the user during the forecasting period.