Recommendation Model Unlearning via Inverse Hessian Adjustment
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Solution Overview
Problem
Current recommendation models fail to efficiently unlearn user data without compromising accuracy or incurring high retraining costs, and lack effective privacy measures for data deletion, especially in industrial-scale applications.
Innovation Solution
A fast heuristic unlearning process for bi-linear recommendation models that allows for accurate data removal without retraining, using Woodbury inverse adjustments and Alternating Least Squares algorithms to maintain model performance and privacy, enabling the recommendation model to perform similarly to a retrained model based on remaining user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete retraining of the recommendation model is performed to remove user data, then privacy requirements are satisfied, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent extracts and removes only the specific user data that needs to be deleted from the training set, rather than retraining the entire model from scratch. This selective extraction approach allows the model to forget specific user information while maintaining the benefits of the complete training dataset, significantly reducing computational overhead while satisfying privacy requirements.
Solution Approach 2:
The patent pre-computes and stores the inverse Hessian matrix during the initial model training phase. This preliminary action enables fast unlearning operations later, as the stored inverse Hessian can be directly utilized in the unlearning process without requiring expensive matrix inversions, thus reducing the time and computational resources needed for data removal.
2Reliability
If complete retraining of the recommendation model is performed to remove user data, then privacy requirements are satisfied, but computational resources and costs increase
Solution Approach 1:
The patent extracts and removes only the specific user data that needs to be deleted from the training set, rather than retraining the entire model from scratch. This selective extraction approach allows the model to forget specific user information while maintaining the benefits of the complete training dataset, significantly reducing computational overhead while satisfying privacy requirements.
Solution Approach 2:
The patent pre-computes and stores the inverse Hessian matrix during the initial model training phase. This preliminary action enables fast unlearning operations later, as the stored inverse Hessian can be directly utilized in the unlearning process without requiring expensive matrix inversions, thus reducing the time and computational resources needed for data removal.
3Reliability
If the recommendation model is unlearned by removing user data, then privacy is improved, but model accuracy may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the model is retrained on the remaining training data after unlearning specific user data. This feedback loop ensures that the model adapts to the updated training set and maintains optimal performance. The retraining process allows the model to learn from the remaining data and compensate for any accuracy loss caused by removing specific user information.
Solution Approach 2:
The patent utilizes parameter changes in the optimization process, specifically leveraging the inverse Hessian matrix to efficiently update model parameters during unlearning. By changing parameters through controlled optimization steps rather than complete retraining, the model can remove user data while minimizing accuracy deterioration and maintaining performance close to the original model.
4Loss of information
If traditional unlearning methods are used, then data removal is achieved, but model performance degrades significantly
Solution Approach 1:
The patent utilizes parameter changes in the optimization process, specifically leveraging the inverse Hessian matrix to efficiently update model parameters during unlearning. By changing parameters through controlled optimization steps rather than complete retraining, the model can remove user data while minimizing accuracy deterioration and maintaining performance close to the original model.
Solution Approach 2:
The patent pre-computes and stores the inverse Hessian matrix during the initial model training phase. This preliminary action enables fast unlearning operations later, as the stored inverse Hessian can be directly utilized in the unlearning process without requiring expensive matrix inversions, thus reducing the time and computational resources needed for data removal.
Data Source
AI summary
The present disclosure describes techniques of performing machine unlearning in a recommendation model. An unlearning process of the recommendation model may be initiated in response to receiving a request for deleting a fraction of user data from any particular user. The recommendation model may be pre-trained to recommend content to users based at least in part on user data. Values of entries in a matrix corresponding to the fraction of user data may be configured as zero. The matrix may comprise entries denoting preferences of users with respect to content items. Confidence values associated with the fraction of user data may be configured as zero to block influence of the fraction of user data on performance of the recommendation model. The unlearning process may be implemented by performing a number of iterations until the recommendation model has converged.