用大模型做可解释的推荐重排序，提升准确率与透明度
LLM as Explainable Re-Ranker for Recommendation System
- 将大模型作为重排序器，结合传统推荐模型提升可解释性
- 两阶段训练使NDCG显著提升，优于零样本基线
- 适合关注推荐系统公平性与透明度的研究者
大语言模型在推荐系统中的应用日益受到关注。传统推荐系统往往缺乏可解释性，并存在热门物品偏好等问题。先前研究也表明，仅使用大模型作为预测器时，其准确率无法达到传统模型水平。为此，我们提出将大模型作为可解释的重排序器，采用混合方法结合传统推荐模型与大模型，以同时提升准确率与可解释性。我们构建了一个数据集用于训练该重排序大模型，并评估了生成结果与人类预期的一致性。通过两阶段训练，我们的模型在关键指标NDCG上显著提升。此外，该重排序器在排名准确率和可解释性方面均优于零样本基线。这些结果表明，将传统推荐模型与大模型结合，有望解决现有系统的局限性，为更可解释、更公平的推荐框架奠定基础。
原文摘要 · Abstract (English)
The application of large language models (LLMs) in recommendation systems has recently gained traction. Traditional recommendation systems often lack explainability and suffer from issues such as popularity bias. Previous research has also indicated that LLMs, when used as standalone predictors, fail to achieve accuracy comparable to traditional models. To address these challenges, we propose to use LLM as an explainable re-ranker, a hybrid approach that combines traditional recommendation models with LLMs to enhance both accuracy and interpretability. We constructed a dataset to train the re-ranker LLM and evaluated the alignment between the generated dataset and human expectations. Leveraging a two-stage training process, our model significantly improved NDCG, a key ranking metric. Moreover, the re-ranker outperformed a zero-shot baseline in ranking accuracy and interpretability. These results highlight the potential of integrating traditional recommendation models with LLMs to address limitations in existing systems and pave the way for more explainable and fair recommendation frameworks.
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