用大模型重排商品推荐，提升准确率和多样性
LLM-Enhanced Reranking for Complementary Product Recommendation
- 用大模型直接重排候选商品，无需重新训练模型
- 在多个数据集上准确率提升至少50%，多样性提升2%
- 适合需要兼顾精准与多样推荐的电商场景
互补商品推荐旨在推荐可协同使用的商品以提升客户价值，是电商中的关键但具有挑战性的任务。尽管现有图神经网络方法在捕捉复杂商品关系方面取得进展，但仍面临准确率与多样性之间的权衡问题，尤其对长尾商品效果不佳。本文提出一种模型无关的方法，利用大语言模型（LLM）增强互补商品推荐的重排能力。不同于以往将LLM用于数据预处理或图增强的做法，本方法直接对已有推荐模型生成的候选商品进行基于LLM的提示策略重排，避免了模型重新训练。在公开数据集上的大量实验表明，该方法有效平衡了推荐的准确率与多样性，平均在各数据集的前几名推荐结果中，准确率指标提升至少50%，多样性指标提升2%。
原文摘要 · Abstract (English)
Complementary product recommendation, which aims to suggest items that are used together to enhance customer value, is a crucial yet challenging task in e-commerce. While existing graph neural network (GNN) approaches have made significant progress in capturing complex product relationships, they often struggle with the accuracy-diversity tradeoff, particularly for long-tail items. This paper introduces a model-agnostic approach that leverages Large Language Models (LLMs) to enhance the reranking of complementary product recommendations. Unlike previous works that use LLMs primarily for data preprocessing and graph augmentation, our method applies LLM-based prompting strategies directly to rerank candidate items retrieved from existing recommendation models, eliminating the need for model retraining. Through extensive experiments on public datasets, we demonstrate that our approach effectively balances accuracy and diversity in complementary product recommendations, with at least 50% lift in accuracy metrics and 2% lift in diversity metrics on average for the top recommended items across datasets.
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