Recommendation System Debiasing via Adversarial Variational Autoencoder
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Solution Overview
Problem
Existing recommendation systems face issues with fairness due to biased training data, leading to unfair recommendations and feedback loops, which traditional fairness constraints struggle to address without degrading the performance of the original model.
Innovation Solution
A two-stage approach involving an identifiable variational autoencoder for disentangling sensitive attributes and an adversarial learning strategy to remove biases, enabling the training of a recommendation model on biased data as if it came from a balanced/unbiased dataset, ensuring balanced fairness and unbiased recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fairness constraints are applied to recommendation systems, then fairness is improved, but model performance deteriorates
Solution Approach 1:
The patent segments the recommendation system into two distinct components: the original recommendation model and a separate fairness constraint model. The fairness constraint model is trained independently on sensitive attributes and then applied as a post-processing step to adjust recommendations, allowing the original model to maintain its performance while fairness constraints are enforced separately.
Solution Approach 2:
The patent introduces an intermediary fairness constraint model that acts as a mediator between the original recommendation system and the final recommendations. This intermediary model processes the sensitive attribute information and generates fairness adjustments without directly modifying the core recommendation algorithm, thus preserving original performance while improving fairness.
2Reliability
If fairness constraints are applied to ensure fair predictions, then fairness is improved, but the original recommendation model performance degrades
Solution Approach 1:
The patent performs preliminary action by training the fairness constraint model in advance on sensitive attribute data before applying it to the recommendation system. This pre-trained fairness model is then used to adjust recommendations without requiring modifications to the original recommendation model during deployment, thus maintaining performance while ensuring fair predictions.
Solution Approach 2:
The patent separates the fairness constraint functionality from the recommendation model by creating an independent fairness model that processes sensitive attributes separately. This segmentation allows the recommendation model to operate independently for performance optimization while the fairness model handles fairness constraints independently.
3Measurement precision
If recommendation systems use biased training data to improve accuracy, then prediction accuracy is improved, but fairness deteriorates due to feedback loops
Solution Approach 1:
The patent introduces an intermediary fairness constraint model that processes sensitive attribute information separately from the main recommendation model. This intermediary component detects and corrects biases in the training data by generating fairness adjustments that are applied to the final recommendations, allowing the system to maintain high prediction accuracy while eliminating fairness issues caused by biased data.
Solution Approach 2:
The patent implements a feedback mechanism where the fairness constraint model continuously monitors recommendations for bias based on sensitive attributes and adjusts future recommendations accordingly. This feedback loop ensures that biased patterns from the training data are corrected in real-time, maintaining both accuracy and fairness.
Data Source
AI summary
Present approach includes methods, computer readable medium, systems, devices for debiasing data. Debiased data is received by or for training a recommendation system. The present approach includes steps of receiving data comprising sensitive-correlated information; obtaining sensitivity representations of the sensitive-correlated information from the data using a plurality of neural networks trained in relation to a set of predetermined context features; deriving a learned representation from the sensitivity representations; and generating a balanced fair prediction from the recommendation system based on the learned representation.