User Embedding Model for Cross-Experiment Treatment Prediction
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Solution Overview
Problem
Existing user embeddings trained for specific applications require significant training time and data, are application-specific, and lack generalizability to other applications, making it difficult to predict user responses to experimental treatments across different systems.
Innovation Solution
An embedding model learns to generate user embeddings based on experimental results from multiple experiments, characterizing users' conditional treatment effects, enabling effective translation to other applications by jointly training with models that predict experimental treatment effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user embeddings are trained for specific applications with significant training time and data, then the user embeddings can accurately predict user outcomes for particular experimental treatments, but the user embeddings become application-specific and cannot be effectively re-used in other applications without further training
Solution Approach 1:
The patent trains user embeddings on multiple heterogeneous experiments simultaneously, enabling the embeddings to serve multiple applications and predict user responses across different treatment types. The unified embedding space captures transferable user characteristics that generalize beyond any single experiment, allowing the same embeddings to be reused across diverse applications without retraining.
Solution Approach 2:
The patent performs preliminary training of user embeddings on available experimental data before new experiments are conducted. This pre-trained embedding captures user characteristics that can be immediately applied to new treatments and experiments, eliminating the need to start training from scratch and enabling faster adaptation to new applications.
2Measurement precision
If user embeddings are trained for specific applications, then the training can capture detailed user characteristics for that application, but significant training time and large data sets are required
Solution Approach 1:
The patent combines multiple experiments and their associated user data into a single unified training process for user embeddings. By merging heterogeneous data sources and training objectives, the system achieves comprehensive user characterization while distributing the training burden across multiple data sources, reducing the time and data required for any single application.
Solution Approach 2:
The patent performs preliminary training of user embeddings on available experimental data before new experiments are conducted. This pre-trained embedding captures user characteristics that can be immediately applied to new treatments and experiments, eliminating the need to start training from scratch and enabling faster adaptation to new applications.
3Measurement precision
If experiments are performed to evaluate different treatments, then user response to particular experiments can be predicted, but the results are not effectively generalizable to other experiments or modifications
Solution Approach 1:
The patent trains user embeddings on multiple heterogeneous experiments simultaneously, enabling the embeddings to serve multiple applications and predict user responses across different treatment types. The unified embedding space captures transferable user characteristics that generalize beyond any single experiment, allowing the same embeddings to be reused across diverse applications without retraining.
Data Source
AI summary
An embedding model is trained to learn latent representations of users describing information related to conditional treatment effect for users relative to different potential treatments. The user embeddings may be used to determine the types of situations in which a user responds differently to different conditions or situations. To train this model, a plurality of experiments with users may be performed to determine user responses to different treatment conditions in the experiments. The conditional treatment effect for users in the experiments may be determined, e.g., with counterfactual predictions of a treatment not experienced by a user in the experiment. The embedding model may be trained with decoders that each predict the conditional treatment effect with respect to one of the experiments, enabling a loss for each experiment with respect to the conditional treatment effect to jointly train the embedding model.