Improving Robustness and Generalization of Explainable Recommendations

核心概念

A novel framework that ensures robustness and generalization of the personalized explanations provided in recommender systems.

要約

The paper presents a framework to improve the robustness and generalization of explainable recommendations. The key insights are:

The framework focuses on feature-aware explainable recommender systems, which leverage user-item features (e.g., from product reviews) to provide explanations for recommendations.

The framework uses adversarial training to make the explainable recommendations more robust to noisy conditions and model-based attacks. It introduces perturbations to the item-feature relationships during training to ensure the model learns to provide reliable explanations even under adversarial conditions.

Experiments on two different feature-aware explainable recommender models (CER and EFM) across three e-commerce datasets show that the proposed framework can improve the robustness of explanations without significantly compromising the clean (non-attacked) performance.

The framework is designed to be flexible and can be easily extended to other feature-aware explainable recommender systems, as the key updates are independent of the internal model structure.

The authors highlight the importance of providing robust and trustworthy explanations, especially in high-stake decision scenarios like healthcare, where malicious attacks on the explanation capability could lead to severe consequences.

統計

Larger perturbations (higher ϵD) can lead to deterioration in the model's performance on the original data.
Smaller values of the loss scale penalty (λ) are not effective in improving robustness, as the defense objective is not weighted enough.
Balancing the learning between the original and perturbed data (e.g., λ=0.5) leads to better generalization and robustness of the explanations.

引用

"Explanations generated along with the recommendations within any RS framework has multiple practical uses."
"It is very important to provide trustworthy explanations within an RS framework, particularly for both consumers and developers."
"We ensure that we can provide robust and trustworthy explanations and to our best knowledge, we present one of the initial works towards the fresh field of robust explanations in RS."

抽出されたキーインサイト

Robust Explainable Recommendation

by Sairamvinay ... 場所 arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01855.pdf

深掘り質問

How can the proposed framework be extended to incorporate fairness considerations in the explainable recommendations

The proposed framework for robust explainable recommendations can be extended to incorporate fairness considerations by integrating fairness metrics and constraints into the training process. Fairness in recommendations ensures that the system does not discriminate against certain groups or individuals based on sensitive attributes such as race, gender, or age. By including fairness constraints, the model can be trained to provide explanations that are not only accurate and robust but also fair and unbiased.
One approach to incorporating fairness considerations is to define fairness metrics that measure the disparity in recommendations across different demographic groups. These metrics can then be included in the loss function alongside the existing objectives of recommendation accuracy and explanation quality. By optimizing for both accuracy, explainability, and fairness simultaneously, the model can learn to provide recommendations that are not only effective but also equitable.
Additionally, techniques such as adversarial training can be adapted to include fairness constraints. Adversarial training can be used to generate counterfactual explanations that highlight potential biases in the recommendations and provide explanations that are sensitive to fairness considerations. By training the model to be robust against adversarial attacks that target fairness, the system can learn to provide explanations that are not only accurate and robust but also fair and unbiased.

What are the potential limitations of the adversarial training approach in ensuring robustness, and how can they be addressed

The adversarial training approach, while effective in improving the robustness of explainable recommendations, has certain limitations that need to be addressed. One potential limitation is the scalability of the approach, as training models with adversarial objectives can be computationally expensive and time-consuming, especially for large datasets and complex models. This scalability issue can be addressed by optimizing the training process, using parallel computing resources, or implementing more efficient adversarial training algorithms.
Another limitation is the potential for overfitting to the adversarial perturbations, where the model becomes too focused on defending against specific attacks and loses generalization capabilities. To mitigate this, techniques such as regularization and data augmentation can be employed to ensure that the model learns robust features that are not overly sensitive to adversarial perturbations.
Furthermore, the adversarial training approach may not fully capture all possible attack scenarios, leading to vulnerabilities that are not addressed during training. To address this limitation, a comprehensive analysis of potential attack vectors and the development of diverse adversarial strategies can help improve the robustness of the model against a wide range of adversarial threats.

How can the insights from this work on robust explainable recommendations be applied to other domains beyond recommender systems, such as interpretable machine learning models in healthcare or finance

The insights from this work on robust explainable recommendations can be applied to other domains beyond recommender systems, such as interpretable machine learning models in healthcare or finance. In healthcare, interpretable machine learning models are crucial for providing explanations for medical decisions and treatment recommendations. By incorporating adversarial training techniques to enhance the robustness of these models, healthcare providers can ensure that the explanations provided are not only accurate but also resistant to potential attacks or manipulations.
Similarly, in finance, interpretable machine learning models are used for credit scoring, fraud detection, and risk assessment. By leveraging the insights from robust explainable recommendations, financial institutions can improve the transparency and trustworthiness of their models. Adversarial training can help identify vulnerabilities in the explanations provided by these models and strengthen them against potential adversarial attacks, ensuring that the decisions made are reliable and unbiased.