Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation

• URL: http://arxiv.org/abs/2507.09188v1
• Date: Sat, 12 Jul 2025 08:15:05 GMT
• Title: Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation
• Authors: Bangcheng Sun, Yazhe Chen, Jilin Yang, Xiaodong Li, Hui Li,
• Abstract summary: Explainable Recommender System (ExRec) provides transparency to the recommendation process, increasing users' trust and boosting the operation of online services.<n>Existing LLM-based ExRec models suffer from profile deviation and high retrieval overhead, hindering their deployment.<n>We propose Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation (REXHA)
• Score: 5.656477996187559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable Recommender System (ExRec) provides transparency to the recommendation process, increasing users' trust and boosting the operation of online services. With the rise of large language models (LLMs), whose extensive world knowledge and nuanced language understanding enable the generation of human-like, contextually grounded explanations, LLM-powered ExRec has gained great momentum. However, existing LLM-based ExRec models suffer from profile deviation and high retrieval overhead, hindering their deployment. To address these issues, we propose Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation (REXHA). Specifically, we design a hierarchical aggregation based profiling module that comprehensively considers user and item review information, hierarchically summarizing and constructing holistic profiles. Furthermore, we introduce an efficient retrieval module using two types of pseudo-document queries to retrieve relevant reviews to enhance the generation of recommendation explanations, effectively reducing retrieval latency and improving the recall of relevant reviews. Extensive experiments demonstrate that our method outperforms existing approaches by up to 12.6% w.r.t. the explanation quality while achieving high retrieval efficiency.

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