FIRE: Faithful Interpretable Recommendation Explanations

• URL: http://arxiv.org/abs/2508.05225v1
• Date: Thu, 07 Aug 2025 10:11:02 GMT
• Title: FIRE: Faithful Interpretable Recommendation Explanations
• Authors: S. M. F. Sani, Asal Meskin, Mohammad Amanlou, Hamid R. Rabiee,
• Abstract summary: Natural language explanations in recommender systems are often framed as a review generation task.<n>Fire is a lightweight and interpretable framework that combines SHAP-based feature attribution with structured, prompt-driven language generation.<n>Our results demonstrate that FIRE not only achieves competitive recommendation accuracy but also significantly improves explanation quality along critical dimensions such as alignment, structure, and faithfulness.
• Score: 2.6499018693213316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language explanations in recommender systems are often framed as a review generation task, leveraging user reviews as ground-truth supervision. While convenient, this approach conflates a user's opinion with the system's reasoning, leading to explanations that may be fluent but fail to reflect the true logic behind recommendations. In this work, we revisit the core objective of explainable recommendation: to transparently communicate why an item is recommended by linking user needs to relevant item features. Through a comprehensive analysis of existing methods across multiple benchmark datasets, we identify common limitations-explanations that are weakly aligned with model predictions, vague or inaccurate in identifying user intents, and overly repetitive or generic. To overcome these challenges, we propose FIRE, a lightweight and interpretable framework that combines SHAP-based feature attribution with structured, prompt-driven language generation. FIRE produces faithful, diverse, and user-aligned explanations, grounded in the actual decision-making process of the model. Our results demonstrate that FIRE not only achieves competitive recommendation accuracy but also significantly improves explanation quality along critical dimensions such as alignment, structure, and faithfulness. This work highlights the need to move beyond the review-as-explanation paradigm and toward explanation methods that are both accountable and interpretable.

Related papers

• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations [9.082885521130617]
  We propose a novel approach combining profile generation via hierarchical interaction summarization (PGHIS) with contrastive prompting for explanation generation (CPEG)<n>Our approach outperforms existing state-of-the-art methods, achieving a great improvement on metrics about explainability (e.g., 5% on GPTScore) and text quality.
  arXiv  Detail & Related papers  (2025-07-08T14:45:47Z)
• CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
  We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
  arXiv  Detail & Related papers  (2025-06-05T17:02:52Z)
• RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning [64.46921169261852]
  RAG-Zeval is a novel end-to-end framework that formulates faithfulness and correctness evaluation as a rule-guided reasoning task.<n>Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments.<n>Experiments demonstrate RAG-Zeval's superior performance, achieving the strongest correlation with human judgments.
  arXiv  Detail & Related papers  (2025-05-28T14:55:33Z)
• RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines [0.7373617024876725]
  RAGXplain is an evaluation framework that quantifies RAG performance and translates these assessments into clear insights.<n> RAGXplain thus bridges quantitative evaluation and practical optimization, empowering users to understand, trust, and enhance their AI systems.
  arXiv  Detail & Related papers  (2025-05-18T17:25:34Z)
• G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation [48.23263809469786]
  We propose a framework using graph retrieval-augmented large language models (LLMs) for explainable recommendation.<n>G-Refer achieves superior performance compared with existing methods in both explainability and stability.
  arXiv  Detail & Related papers  (2025-02-18T06:42:38Z)
• Explainable Recommender with Geometric Information Bottleneck [25.703872435370585]
  We propose to incorporate a geometric prior learnt from user-item interactions into a variational network. Latent factors from an individual user-item pair can be used for both recommendation and explanation generation. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender.
  arXiv  Detail & Related papers  (2023-05-09T10:38:36Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Explainability in Music Recommender Systems [69.0506502017444]
  We discuss how explainability can be addressed in the context of Music Recommender Systems (MRSs) MRSs are often quite complex and optimized for recommendation accuracy. We show how explainability components can be integrated within a MRS and in what form explanations can be provided.
  arXiv  Detail & Related papers  (2022-01-25T18:32:11Z)
• Counterfactual Explainable Recommendation [22.590877963169103]
  We propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. CountER seeks simple (low complexity) and effective (high strength) explanations for the model decision. Results show that our model generates more accurate and effective explanations than state-of-the-art explainable recommendation models.
  arXiv  Detail & Related papers  (2021-08-24T06:37:57Z)
• Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability [22.58823484394866]
  Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features.
  arXiv  Detail & Related papers  (2020-07-12T23:49:12Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.