Recommender systems have become integral to many online services, leveraging user data to provide personalized recommendations. However, as these systems grow in complexity, understanding the rationale behind their recommendations becomes increasingly difficult. Explainable Artificial Intelligence (XAI) has emerged as a crucial field addressing this challenge, particularly in ensuring transparency and trustworthiness in automated decision-making processes. In this article, we introduce Learning to eXplain Recommendations (LXR) , a scalable, model-agnostic framework designed to generate counterfactually correct explanations for recommender systems. LXR generates explanations for recommendations produced by any differentiable recommender system. By leveraging both factual and counterfactual loss terms, LXR offers robust, accurate, and computationally efficient explanations that reflect the model’s internal decision-making process. A key feature of LXR is its focus on the factual correctness of explanations through counterfactual reasoning, bridging the gap between plausible and accurate explanations. Unlike traditional approaches that rely on exhaustive perturbations of user data, LXR uses a self-supervised learning method to generate explanations efficiently, without sacrificing accuracy. LXR operates in two stages: a pre-training step and a novel Inference-Time Fine-tuning (ITF) step that refines explanations at the individual recommendation level, significantly improving accuracy with minimal computational overhead. Additionally, LXR is applied to hybrid recommender models incorporating demographic data, demonstrating its versatility across real-world scenarios. Finally, we also showcase LXR’s ability to explain recommendations at various ranks within a user’s recommendation list. As a secondary contribution, we introduce several novel evaluation metrics, inspired by saliency maps from computer vision, to rigorously assess the counterfactual correctness of explanations in recommender systems. Our results demonstrate that LXR sets a new benchmark for explainability, providing accurate, transparent, and interpretable explanations. The code is available on our GitHub repository: https://github.com/DeltaLabTLV/LXR_ .

Counterfactual evaluation provides a promising framework for assessing explanation fidelity in recommender systems, but perturbation metrics adapted from computer vision suffer three key limitations: (1) they conflate explaining and contradictory features, (2) they average over entire user histories instead of prioritizing concise, high-impact explanations, and (3) they use fixed-percentage perturbations, leading to inconsistencies across users.We introduce refined counterfactual metrics that focus on the most relevant explaining features, exclude contradictory elements, and assess fidelity at a fixed explanation length, ensuring a more consistent and interpretable evaluation.Our code is at: https:// github.com/DeltaLabTLV/FidelityMetrics4XRec

We present Learning Attributions (LA), a novel method for explaining language models. The core idea behind LA is to train a dedicated attribution model that functions as a surrogate explainer for the language model. This attribution model is designed to identify which tokens are most influential in driving the model's predictions. By optimizing the attribution model to mask the minimal amount of information necessary to induce substantial changes in the language model's output, LA provides a mechanism to understand which tokens in the input are critical for the model's decisions. We demonstrate the effectiveness of LA across several language models, highlighting its superiority over multiple state-of-the-art explanation methods across various datasets and evaluation metrics.

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix

We present Learning to Explain (LTX), a model-agnostic framework designed for providing post-hoc explanations for vision models. The LTX framework introduces an “explainer” model that generates explanation maps, highlighting the crucial regions that justify the predictions made by the model being explained. To train the explainer, we employ a two-stage process consisting of initial pretraining followed by per-instance finetuning. During both stages of training, we utilize a unique configuration where we compare the explained model’s prediction for a masked input with its original prediction for the unmasked input. This approach enables the use of a novel counterfactual objective, which aims to anticipate the model’s output using masked versions of the input image. Importantly, the LTX framework is not restricted to a specific model architecture and can provide explanations for both Transformer-based and convolutional models. Through our evaluations, we demonstrate that LTX significantly outperforms the current state-of-the-art in explainability across various metrics. Our code is available at: https://github.comLTX-CodeLTX

We introduce Stochastic Integrated Explanations (SIX) - a general method for explaining predictions made by vision models. SIX employs stochastic integration on the internal representations across different network layers, producing explanation maps at various scales. The primary innovation of SIX is the introduction of randomness to the integration process by modeling the baseline representation as a random tensor. Through iterative sampling from the baseline distribution, SIX generates a diverse set of explanation maps, allowing the selection of the best-performing map based on a specific metric of interest. Extensive evaluations on various model architectures showcase the superior performance of SIX compared to state-of-the-art explanation methods, affirming its effectiveness across multiple metrics. Our code is available at: https://github.com/six-icdm/six