In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in research around this subject are Graph Neural Networks (GNNs) and Foundation Models (FMs). While GNNs emerged as a popular solution in industry for powering personalization at scale, FMs have only recently caught attention for their promising performance in personalization tasks like ranking and retrieval. In this paper, we present a graph-based foundation modeling approach tailored to personalization. Central to this approach is a Heterogeneous GNN (HGNN) designed to capture multi-hop content and consumption relationships across a range of recommendable item types. To ensure the generality required from a Foundation Model, we employ a Large Language Model (LLM) text-based featurization of nodes that accommodates all item types, and construct the graph using co-interaction signals, which inherently transcend content specificity. To facilitate practical generalization, we further couple the HGNN with an adaptation mechanism based on a two-tower (2T) architecture, which also operates agnostically to content type. This multi-stage approach ensures high scalability; while the HGNN produces general purpose embeddings, the 2T component models in a continuous space the sheer size of user-item interaction data. Our comprehensive approach has been rigorously tested and proven effective in delivering recommendations across a diverse array of products within a real-world, industrial audio streaming platform.

翻译：在个性化领域，整合消费信号和基于内容的表示等多源信息，日益成为构建先进解决方案的关键。围绕这一主题，当前两大研究趋势分别是图神经网络（GNNs）和基础模型（FMs）。虽然GNNs已作为规模化驱动个性化的流行解决方案在工业界广泛应用，但FMs近期才因其在排序和检索等个性化任务中的出色表现而受到关注。本文提出了一种面向个性化的基于图的基础建模方法。该方法的核心理念是设计一个异构GNN（HGNN），用于捕获跨多种可推荐内容类型的多跳内容与消费关系。为确保基础模型所需的通用性，我们采用基于大型语言模型（LLM）的文本特征化方法对节点进行表征，该方法兼容所有内容类型，并利用天然超越内容特异性的共交互信号构建图结构。为促进实际场景中的泛化能力，我们将HGNN与基于双塔（2T）架构的适应机制相结合，该机制同样对内容类型无感知。这种多阶段方法确保了高度可扩展性：HGNN生成通用嵌入，而2T组件则在连续空间中建模海量用户-物品交互数据。我们对该综合性方法进行了严格测试，并在真实工业音频流平台上的多种产品推荐场景中验证了其有效性。