协同信号：通过图神经网络利用共同参与和语义链接
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
AI总结：
针对仅依赖共同参与导致新实体和冷门实体嵌入质量低的问题，提出 SemanticGNN，在大规模知识图谱中融合协同与语义信号，并通过关系感知注意力和分布式训练提升相似性判断性能。
摘要：
给定一组候选实体（例如电影名称），识别相似实体的能力是许多推荐系统的核心能力。最常见的实现方式是协同过滤方法，即如果用户足够频繁地共同参与一对实体，那么它们的嵌入应当相似。然而，仅依赖共同参与数据可能导致新实体和不受欢迎实体的嵌入质量较低。我们在 Netflix 的推荐系统场景中研究了这一问题。我们观察到，存在丰富的语义信息，例如类型、内容成熟度级别、主题等，这些信息补充了共同参与信号，并为相似性模型提供了可解释性。为了从整体上利用这两种数据源学习实体相似性，我们提出了一种名为 SemanticGNN 的新型基于图的方法。SemanticGNN 在大规模知识图谱中对实体、语义概念、协同边和语义边进行建模，并在其上进行表示学习。我们的关键技术贡献有两方面：（1）我们开发了一种新型的关系感知注意力图神经网络（GNN），以处理图中关系类型的不平衡分布；（2）为了处理具有数百万节点和数十亿边的网络级图数据，我们开发了一种新型的分布式图训练范式。所提出的模型已成功部署在 Netflix 内部，实证实验表明，它在相似性判断任务上的性能提升最高可达 35%。
英文摘要：
Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if users co-engage with a pair of entities frequently enough, the embeddings should be similar. However, relying on co-engagement data alone can result in lower-quality embeddings for new and unpopular entities. We study this problem in the context recommender systems at Netflix. We observe that there is abundant semantic information such as genre, content maturity level, themes, etc. that complements co-engagement signals and provides interpretability in similarity models. To learn entity similarities from both data sources holistically, we propose a novel graph-based approach called SemanticGNN. SemanticGNN models entities, semantic concepts, collaborative edges, and semantic edges within a large-scale knowledge graph and conducts representation learning over it. Our key technical contributions are twofold: (1) we develop a novel relation-aware attention graph neural network (GNN) to handle the imbalanced distribution of relation types in our graph; (2) to handle web-scale graph data that has millions of nodes and billions of edges, we develop a novel distributed graph training paradigm. The proposed model is successfully deployed within Netflix and empirical experiments indicate it yields up to 35% improvement in performance on similarity judgment tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
