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.

翻译：给定用户查询后找到相关商品是电子商务平台的关键环节，这能驱动购物行为并产生收益。其挑战在于准确预测查询与商品之间的关联性。近年来，利用大语言模型挖掘查询与商品间的常识知识在提升推荐性能方面展现出良好前景。然而，此类方法在推理阶段需要密集的实时大语言模型解码，训练阶段需要人工标注和监督微调，导致成本高昂。为在利用大语言模型常识推理能力处理各类电子商务任务的同时提升效率，我们提出了高效常识增强推荐增强器（E-CARE），该方法无需监督微调或人工标注。通过利用编码了强大语言模型大多数推理模式的推理因子图，E-CARE增强的推荐模型无需实时大语言模型解码即可获取常识推理能力。在两个下游任务上的实验表明，P@5指标最高提升了12.1%。