Mitigating Dual Latent Confounding Biases in Recommender Systems Using Instrumental Variables and Identifiable Variational Auto-Encoders (IViDR)

핵심 개념

This research paper introduces IViDR, a novel debiasing method for recommender systems that effectively mitigates dual latent confounding biases stemming from unobserved factors influencing both user-item interactions and item exposure.

초록

• Bibliographic Information: Deng, J., Chen, Q., Cheng, D., Li, J., Liu, L., & Du, X. (2024). Mitigating Dual Latent Confounding Biases in Recommender Systems. Conference’25.

• Research Objective: This paper addresses the challenge of dual latent confounding biases in recommender systems, aiming to develop a robust method that mitigates biases arising from unobserved factors influencing both user-item interactions and item exposure.

• Methodology: The researchers propose IViDR, a novel debiasing method that integrates Instrumental Variables (IV) and an identifiable Variational Auto-Encoder (iVAE). IViDR leverages user feature embeddings as IVs to reconstruct treatment variables, generating debiased interaction data. Subsequently, an iVAE infers identifiable latent representations from proxy variables, interaction data, and the debiased data to mitigate confounding biases.

• Key Findings: Extensive experiments on synthetic and real-world datasets demonstrate IViDR's superiority over state-of-the-art deconfounding methods. IViDR consistently achieves significant improvements in recommendation accuracy and bias reduction, as evidenced by superior performance across evaluation metrics like NDCG@5 and RECALL@5.

• Main Conclusions: IViDR effectively mitigates dual latent confounding biases in recommender systems, leading to more accurate and unbiased recommendations. The integration of IVs and iVAE allows for robust debiasing by addressing both observed and unobserved confounding factors.

• Significance: This research significantly contributes to the field of recommender systems by introducing a practical and effective solution for mitigating dual latent confounding biases. IViDR's ability to handle both types of biases enhances the reliability and fairness of recommendations.

• Limitations and Future Research: While IViDR demonstrates strong performance, its reliance on the availability and quality of IVs and proxy variables poses a limitation. Future research could explore methods for automatically identifying suitable IVs and proxy variables or developing alternative approaches that relax these requirements. Additionally, investigating IViDR's applicability in more complex recommendation scenarios, such as those involving sequential or contextual information, presents promising avenues for future work.

통계

The Coat dataset consists of 290 users, 300 items, 6,960 biased data points, and 4,640 unbiased data points.
The Yahoo!R3 dataset consists of 5,400 users, 1,000 items, 129,179 biased data points, and 54,000 unbiased data points.
The KuaiRand dataset consists of 23,533 users, 6,712 items, 1,413,574 biased data points, and 954,814 unbiased data points.

인용구

핵심 통찰 요약

Mitigating Dual Latent Confounding Biases in Recommender Systems

by Jianfeng Den... 게시일 arxiv.org 10-17-2024

https://arxiv.org/pdf/2410.12451.pdf

더 깊은 질문

How can the ethical implications of debiasing techniques in recommender systems be addressed, ensuring fairness and mitigating potential unintended consequences?

Debiasing techniques in recommender systems, while aiming to improve accuracy and fairness, can inadvertently introduce ethical challenges. Addressing these implications requires a multi-faceted approach:

Defining Fairness: There's no one-size-fits-all definition of "fairness." It's crucial to establish clear, context-specific fairness metrics for the recommender system. For example, fairness in a job recommendation system might mean ensuring representation from diverse socioeconomic backgrounds, while in a news recommender, it might involve mitigating political bias.

Transparency and Explainability: Black-box debiasing methods can perpetuate unfairness without clear understanding. Transparent and explainable debiasing techniques allow for scrutiny, identification of biases in the debiasing process itself, and potential for recourse if users feel unfairly treated.

Data Bias Mitigation: Debiasing shouldn't solely focus on the algorithm. Addressing biases in the training data itself is paramount. This includes techniques like data augmentation to improve representation of underrepresented groups, careful feature selection to avoid proxies for sensitive attributes, and pre-processing methods to mitigate historical biases.

Continuous Monitoring and Evaluation: Debiasing is not a one-time fix. Continuous monitoring of the system's outputs for bias is essential. This involves tracking fairness metrics over time, analyzing user feedback for potential disparities, and adapting the debiasing techniques or data as needed.

User Control and Awareness: Providing users with some level of control over their recommendations can enhance fairness perceptions. This could involve allowing users to adjust the importance of certain factors in the recommendation process or providing transparency into why specific recommendations are made.

Interdisciplinary Collaboration: Addressing ethical implications requires collaboration between computer scientists, ethicists, social scientists, and domain experts. This ensures a holistic understanding of potential biases, fairness considerations, and unintended consequences.
By incorporating these strategies, we can strive towards more ethical and fair debiasing techniques in recommender systems.

Could the reliance on IVs and proxy variables in IViDR be circumvented by exploring alternative debiasing approaches that leverage different assumptions or data characteristics?

Yes, the reliance on IVs and proxy variables in IViDR, while effective under its specific assumptions, can be limiting in scenarios where these assumptions don't hold or appropriate variables are unavailable. Here are some alternative debiasing approaches that circumvent this reliance:

Causal Embedding Methods: These methods aim to learn representations of data that are invariant to the effects of confounding. Techniques like deep latent variable models with counterfactual regularization or adversarial training can be used to disentangle causal factors from confounders, reducing bias without explicitly relying on IVs or proxies.

Domain Adversarial Networks (DANs): DANs, originally designed for domain adaptation, can be repurposed for debiasing. By training a feature extractor that minimizes the ability of a discriminator to distinguish between data from different groups (e.g., based on sensitive attributes), DANs can learn representations that are less biased by these attributes.

Fairness Constraints in Optimization: Instead of relying on IVs, fairness constraints can be directly incorporated into the optimization objective of the recommender system. This involves defining fairness metrics and adding regularization terms to the loss function that penalize unfair outcomes.

Propensity Score Matching without IVs: While IViDR uses IVs for treatment reconstruction, propensity score matching techniques can be used to create balanced groups of users based on their estimated probability of receiving a recommendation, even without explicit IVs. This can help mitigate confounding bias by comparing similar users who received different recommendations.

Leveraging Temporal Information: If longitudinal data is available, analyzing changes in user preferences over time can provide insights into potential biases. For example, sudden shifts in recommendation patterns after exposure to certain content might indicate bias. This temporal information can be used to develop debiasing techniques without relying solely on IVs or proxies.
Exploring these alternative approaches broadens the applicability of debiasing techniques to situations where IViDR's assumptions might not hold, leading to more robust and flexible solutions.

How can the principles of IViDR be extended beyond recommender systems to address confounding biases in other machine learning applications, such as personalized medicine or social network analysis?

The core principles of IViDR, namely leveraging instrumental variables and identifiable variational autoencoders to mitigate latent confounding biases, hold significant potential for generalization to other machine learning applications beyond recommender systems. Here's how:
Personalized Medicine:

Treatment Effect Estimation: IViDR can be adapted to estimate the causal effects of different treatments (e.g., drugs, therapies) on patient outcomes. For instance, genetic markers could serve as IVs, linked to treatment assignment but not directly influencing the outcome. The iVAE could then be used to model latent confounders like patient lifestyle or environmental factors, leading to more accurate treatment effect estimations.

Disease Risk Prediction: In predicting disease risk, IViDR can help disentangle the causal relationships between risk factors and disease onset. For example, socioeconomic factors could act as IVs, influencing exposure to environmental hazards but not directly causing the disease. The iVAE can model latent confounders like genetic predispositions, improving risk prediction accuracy.
Social Network Analysis:

Influence Maximization: IViDR can be applied to identify influential users in social networks for targeted interventions. For example, network centrality measures could serve as IVs, correlated with influence but not directly affecting the outcome of interest (e.g., adoption of a behavior). The iVAE can model latent confounders like homophily or community structures, leading to more effective influence maximization strategies.

Spread of Misinformation: In studying the spread of misinformation, IViDR can help understand the causal impact of different interventions. For instance, exposure to fact-checking websites could be the treatment, with IVs like news consumption habits. The iVAE can model latent confounders like pre-existing beliefs or social network structures, providing insights into effective misinformation mitigation strategies.
Key Considerations for Generalization:

Context-Specific IVs and Proxies: The success of IViDR hinges on identifying valid IVs and relevant proxy variables for the specific application domain. This requires careful consideration of the causal mechanisms at play and domain expertise.

Assumption Validation: The assumptions underlying IViDR, such as the exclusion restriction (IVs only affecting the outcome through the treatment), need to be carefully validated in the new context. Violations of these assumptions can lead to biased estimations.

Interpretability and Actionability: In sensitive domains like healthcare or social policy, interpretability of the learned representations and actionability of the insights are crucial. The iVAE's ability to provide identifiable representations of latent confounders can be particularly valuable in these contexts.
By carefully adapting the principles of IViDR and addressing these considerations, we can leverage its power to mitigate confounding biases and gain valuable causal insights in a wide range of machine learning applications.