Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)
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| Challenge: | Existing models learn user and item embeddings and generate reasons based on these embedds. |
| Approach: | They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons. |
| Outcome: | Extensive experiments on three datasets prove the proposed model is superior to existing models. |
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| Challenge: | Existing models of explainable recommendation use user reviews as ground truths, but in practice, a large amount of users may not leave reviews after purchasing items. |
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Enhancing Recommendation Explanations through User-Centric Refinement (2025.findings-emnlp)
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| Challenge: | Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users. |
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| Challenge: | Recent years have witnessed the successful application of natural language generation. |
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Improving Personalized Explanation Generation through Visualization (2022.acl-long)
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
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Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021.findings-emnlp)
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| Challenge: | Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. |
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
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XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)
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| Challenge: | Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items. |
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Data-Efficient Concept Extraction from Pre-trained Language Models for Commonsense Explanation Generation (2022.findings-emnlp)
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| Challenge: | Existing methods to extract concepts from pre-trained language models are not suitable for commonsense explanation generation. |
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ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning (2025.findings-naacl)
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| Challenge: | Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while simultaneously providing human-intelligible explanations. |
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