用预存推理图提升电商推荐效率，无需微调或人工标注。
E-CARE: An Efficient LLM-based Commonsense-Augmented Framework for E-Commerce
- 构建推理因子图替代实时大模型推理，降低计算开销。
- 在两个下游任务中精度最高提升12.1%。
- 适合追求高效、低成本的电商推荐系统部署。
基于用户查询精准匹配商品是电商平台的核心，直接影响购物行为与收益。当前方法通过大语言模型（LLMs）挖掘查询与商品间的常识知识，虽有效提升推荐性能，但存在推理时需频繁调用LLM导致高成本，且训练阶段依赖人工标注与监督微调（SFT）。为在保持常识推理能力的同时提升效率，我们提出E-CARE框架，无需SFT也无需人工标注。该框架通过编码强大LLM的推理模式形成推理因子图，使推荐模型可直接访问常识推理能力，无需实时调用LLM。在两个下游任务上的实验表明，精度@5最高提升12.1%。
原文摘要 · Abstract (English)
Finding relevant products given a user query is pivotal to an e-commerce platform, as it can drive shopping behavior and generate revenue. The challenge lies in accurately predicting the correlation between queries and products. Recently, mining commonsense knowledge between queries and products using Large Language Models (LLMs) has shown promising results in boosting recommendation performance. However, such methods incur high costs due to intensive real-time LLM decoding during inference, as well as human annotation and potential Supervised Fine-Tuning (SFT) during training. To boost efficiency while leveraging LLMs' commonsense reasoning for various e-commerce tasks, we propose the Efficient Commonsense-Augmented Recommendation Enhancer (E-CARE), which requires neither SFT nor human annotation. The recommendation models augmented with E-CARE can access commonsense reasoning by leveraging a reasoning factor graph that encodes most of the reasoning schema from powerful LLMs, without requiring real-time LLM decoding. The experiments on 2 downstream tasks show improvements of up to 12.1% in precision@5.
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