Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations

• URL: http://arxiv.org/abs/2507.06044v1
• Date: Tue, 08 Jul 2025 14:45:47 GMT
• Title: Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
• Authors: Yibin Liu, Ang Li, Shijian Li,
• Abstract summary: 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.
• Score: 9.082885521130617
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainable recommendations, which use the information of user and item with interaction to generate a explanation for why the user would interact with the item, are crucial for improving user trust and decision transparency to the recommender system. Existing methods primarily rely on encoding features of users and items to embeddings, which often leads to information loss due to dimensionality reduction, sparse interactions, and so on. With the advancements of large language models (LLMs) in language comprehension, some methods use embeddings as LLM inputs for explanation generation. However, since embeddings lack inherent semantics, LLMs must adjust or extend their parameters to interpret them, a process that inevitably incurs information loss. To address this issue, we propose a novel approach combining profile generation via hierarchical interaction summarization (PGHIS), which leverages a pretrained LLM to hierarchically summarize user-item interactions, generating structured textual profiles as explicit representations of user and item characteristics. Additionally, we propose contrastive prompting for explanation generation (CPEG) which employs contrastive learning to guide another reasoning language models in producing high-quality ground truth recommendation explanations. Finally, we use the textual profiles of user and item as input and high-quality explanation as output to fine-tune a LLM for generating explanations. Experimental results on multiple datasets demonstrate that 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. Furthermore, our generated ground truth explanations achieve a significantly higher win rate compared to user-written reviews and those produced by other methods, demonstrating the effectiveness of CPEG in generating high-quality ground truths.

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