E-CARE: An Efficient LLM-based Commonsense-Augmented Framework for E-Commerce

TL;DR

E-CARE introduces an efficient framework that leverages LLMs' commonsense reasoning via a reasoning factor graph, significantly improving e-commerce recommendation accuracy without costly real-time decoding or human annotation.

Contribution

The paper presents E-CARE, a novel approach that encodes LLM-derived commonsense reasoning into a factor graph, eliminating the need for fine-tuning or human annotation while enhancing recommendation performance.

Findings

Up to 12.1% improvement in precision@5

Effective commonsense reasoning without real-time LLM decoding

No need for supervised fine-tuning or human annotation

Abstract

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.