An Automatic Graph Construction Framework based on Large Language Models for Recommendation

📄 arXiv: 2412.18241v2 📥 PDF

作者: Rong Shan, Jianghao Lin, Chenxu Zhu, Bo Chen, Menghui Zhu, Kangning Zhang, Jieming Zhu, Ruiming Tang, Yong Yu, Weinan Zhang

分类: cs.IR, cs.AI

发布日期: 2024-12-24 (更新: 2025-07-01)

备注: Accepted by KDD'25

💡 一句话要点

提出AutoGraph框架以自动化图构建解决推荐系统中的效率问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 图神经网络 推荐系统 自动化图构建 大型语言模型 潜在因子 元路径聚合 华为广告平台

📋 核心要点

  1. 现有的GNN推荐方法多集中于模型优化，忽视了图构建阶段的重要性，导致构建效率低下。
  2. 本文提出AutoGraph框架，利用大型语言模型自动推断用户偏好和物品知识，从而高效构建推荐图。
  3. 在三组真实数据集上的实验表明，AutoGraph相比现有基线方法具有显著的效率和效果提升。

📝 摘要（中文）

图神经网络（GNNs）已成为从图结构数据中学习的最先进方法，尤其在推荐系统中。然而，现有方法多集中于模型结构和学习策略的优化，忽视了图构建阶段的重要性。传统的图构建方法依赖于特定规则或众包，存在简化或劳动密集的问题。本文提出了AutoGraph，一个基于大型语言模型（LLMs）的自动图构建框架，旨在克服现有方法的局限性。通过推断用户偏好和物品知识，AutoGraph生成语义向量，并利用向量量化提取潜在因子，最终构建出具有深层全局语义的图。实验结果表明，AutoGraph在多个真实数据集上表现优异，并已在华为广告平台部署，显著提升了推荐效果。

🔬 方法详解

问题定义：本文旨在解决现有GNN推荐方法在图构建阶段的低效率和信息缺失问题。传统方法依赖于简单规则或众包，导致构建图的过程过于繁琐和不够智能。

核心思路：AutoGraph框架通过大型语言模型推断用户偏好和物品知识，生成语义向量，并利用向量量化提取潜在因子，从而自动化图构建过程。

技术框架：AutoGraph的整体架构包括三个主要模块：首先，使用LLMs推断用户和物品的语义信息；其次，通过向量量化提取潜在因子；最后，将这些潜在因子作为额外节点连接用户和物品节点，形成具有深层语义的推荐图。

关键创新：AutoGraph的创新在于将大型语言模型应用于图构建，克服了传统方法的局限性，能够更全面地考虑上下文信息，提升了图的全局视角。

关键设计：在设计中，AutoGraph采用了元路径基础的消息聚合策略，有效整合语义和协作信息，确保信息的全面性和准确性。

🖼️ 关键图片

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📊 实验亮点

在与现有基线方法的对比实验中，AutoGraph在华为广告平台上实现了2.69%的RPM提升和7.31%的eCPM提升，证明了其在实际应用中的有效性和优越性。

🎯 应用场景

AutoGraph框架具有广泛的应用潜力，尤其在推荐系统、广告投放和个性化服务等领域。通过自动化图构建，能够显著提升推荐的准确性和效率，降低人工干预的需求，未来可能推动更多智能推荐系统的开发与应用。

📄 摘要（原文）

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph construction stage. Earlier works for graph construction usually rely on speciffic rules or crowdsourcing, which are either too simplistic or too labor-intensive. Recent works start to utilize large language models (LLMs) to automate the graph construction, in view of their abundant open-world knowledge and remarkable reasoning capabilities. Nevertheless, they generally suffer from two limitations: (1) invisibility of global view (e.g., overlooking contextual information) and (2) construction inefficiency. To this end, we introduce AutoGraph, an automatic graph construction framework based on LLMs for recommendation. Specifically, we first use LLMs to infer the user preference and item knowledge, which is encoded as semantic vectors. Next, we employ vector quantization to extract the latent factors from the semantic vectors. The latent factors are then incorporated as extra nodes to link the user/item nodes, resulting in a graph with in-depth global-view semantics. We further design metapath-based message aggregation to effectively aggregate the semantic and collaborative information. The framework is model-agnostic and compatible with different backbone models. Extensive experiments on three real-world datasets demonstrate the efficacy and efffciency of AutoGraph compared to existing baseline methods. We have deployed AutoGraph in Huawei advertising platform, and gain a 2.69% improvement on RPM and a 7.31% improvement on eCPM in the online A/B test. Currently AutoGraph has been used as the main trafffc model, serving hundreds of millions of people.