LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations

TL;DR

This paper introduces a novel framework that integrates graph edge information into large language models to enhance personalized recommendations by combining graph structure with text generation capabilities.

Contribution

It proposes a new method to incorporate graph edge data into LLMs through prompt and attention innovations, bridging the gap between graph and text-based recommendation approaches.

Findings

Improved recommendation relevance and quality on real-world datasets.

Effective integration of first- and second-order graph relationships into LLMs.

Enhanced understanding of graph connectivity in recommendation tasks.

Abstract

Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large language models, representing a textual generative perspective, excel at modeling user languages, understanding behavioral contexts, capturing user-item semantic relationships, analyzing textual sentiments, and generating coherent and contextually relevant texts as recommendations. However, there is a gap between the connected graph perspective and the text generation perspective as the task formulations are different. A research question arises: how can we effectively integrate the two perspectives for more personalized recsys? To fill this gap, we propose to incorporate graph-edge information into LLMs via prompt and attention innovations. We reformulate recommendations as a probabilistic generative problem using prompts. We develop a framework to incorporate graph edge information from the prompt and attention mechanisms for graph-structured LLM recommendations. We develop a new prompt design that brings in both first-order and second-order graph relationships; we devise an improved LLM attention mechanism to embed direct the spatial and connectivity information of edges. Our evaluation of real-world datasets demonstrates the framework's ability to understand connectivity information in graph data and to improve the relevance and quality of recommendation results.