Friend Story Ranking with Edge-Contextual Local Graph Convolutions
Friend Story Ranking with Edge-Contextual Local Graph Convolutions
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DOI：
10.1145/3488560.3498398
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Xianfeng Tang;Yozen Liu;Xinran He;Suhang Wang;Neil Shah
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文献类型：
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作者：
Xianfeng Tang;Yozen Liu;Xinran He;Suhang Wang;Neil Shah
社交平台为用户之间的交流创造了新的现代方式。近年来，多个平台都引入了“故事”功能，可以播放短暂的多媒体内容。具体来说，“朋友故事”，或旨在由一个人的亲密朋友消费的故事，是一个受欢迎的功能，通过允许人们（视觉上）看到他们的朋友和家人在做什么来促进重要的用户与用户的互动。为给定用户呈现朋友故事的关键挑战是对每个查看用户的朋友进行排名，以有效地优先考虑和路由有限的用户注意力。在这项工作中，我们从图表示学习的角度探索了朋友故事排名的新问题。更一般地说，我们的问题是一个链接排名任务，其中对现有链接（关系）进行推断，这与基于公共节点或图形的任务或链接预测任务不同，后者的目标是对不存在的链接进行推断。我们提出了ELR，边缘上下文的方法，仔细考虑局部图结构，局部边缘类型和方向性之间的差异，以及丰富的边缘属性，建立在骨干图卷积。ELR通过考虑和关注相邻节点来处理社会稀疏性挑战，并在本地周围的egonet结构中加入多种边缘类型。我们在两个大型国家级数据集上验证了ELR，这些数据集拥有数百万用户和来自Snapchat的数千万链接。ELR表现出上级性能优于替代品的8%和5%的误差减少测量的MSE和MAE相应。进一步的通用性，数据效率和烧蚀实验证实了ELR的优势。
Social platforms have paved the way in creating new, modern ways for users to communicate with each other. In recent years, multiple platforms have introduced ''Stories'' features, which enable broadcasting of ephemeral multimedia content. Specifically, ''Friend Stories,'' or Stories meant to be consumed by one's close friends, are a popular feature, promoting significant user-user interactions by allowing people to see (visually) what their friends and family are up to. A key challenge in surfacing Friend Stories for a given user, is in ranking over each viewing user's friends to efficiently prioritize and route limited user attention. In this work, we explore the novel problem of Friend Story Ranking from a graph representation learning perspective. More generally, our problem is a link ranking task, where inferences are made over existing links (relations), unlike common node or graph-based tasks, or link prediction tasks, where the goal is to make inferences about non-existing links. We propose ELR, an edge-contextual approach which carefully considers local graph structure, differences between local edge types and directionality, and rich edge attributes, building on the backbone of graph convolutions. ELR handles social sparsity challenges by considering and attending over neighboring nodes, and incorporating multiple edge types in local surrounding egonet structures. We validate ELR on two large country-level datasets with millions of users and tens of millions of links from Snapchat. ELR shows superior performance over alternatives by 8% and 5% error reduction measured by MSE and MAE correspondingly. Further generality, data efficiency and ablation experiments confirm the advantages of ELR.