E-CARE: An Efficient LLM-based Commonsense-Augmented Framework for E-Commerce
This work addresses efficiency and cost issues in using LLMs for e-commerce recommendations, offering a domain-specific solution that is incremental in improving existing methods.
The paper tackles the challenge of accurately predicting query-product correlations in e-commerce by proposing E-CARE, an efficient framework that leverages commonsense reasoning from LLMs without real-time decoding or fine-tuning, achieving up to 12.1% improvement in precision@5 on two downstream tasks.
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.