用分层聚合提升推荐解释生成质量，降低检索延迟
Retrieval-Augmented Recommendation Explanation Generation with Hierarchical Aggregation
- 分层聚合用户与商品评论，构建更完整的用户画像
- 采用双伪文档查询，检索效率提升，相关评论召回率更高
- 在解释质量上领先12.6%，适合需要高可信推荐的场景
可解释推荐系统（ExRec）通过提供透明的推荐过程，增强用户信任并提升在线服务运营效果。随着大语言模型（LLM）的兴起，其广泛的世界知识和细腻的语言理解能力使得生成类人、上下文相关的解释成为可能，推动了基于LLM的ExRec快速发展。然而，现有方法存在用户画像偏差和高检索开销问题，限制了实际部署。为此，我们提出基于分层聚合的检索增强型推荐解释生成方法（REXHA）。具体地，设计了一种分层聚合的画像构建模块，综合考虑用户与商品的评论信息，层次化总结并构建整体画像；同时引入一种高效的检索模块，使用两种伪文档查询来检索相关评论，有效降低检索延迟并提升相关评论召回率。大量实验表明，本方法在解释质量上相比现有方法最高提升12.6%，同时保持高检索效率。
原文摘要 · Abstract (English)
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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