论文标题
TWHIN：嵌入Twitter异质信息网络以获取个性化建议
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
论文作者
论文摘要
社交网络（例如Twitter）形成了一个异质信息网络（HIN），其中节点代表域实体（例如，用户，内容，广告商等），边缘代表了许多实体交互之一（例如，用户重新分布内容或“关注”另一个）。来自多种关系类型的交互可以编码有关未完全捕获单个关系的社交网络实体的有价值信息；例如，用户偏爱帐户遵循的偏好可能取决于用户 - 容量参与性交互及其所关注的其他用户。在这项工作中，我们研究了Twitter Hin（Twhin）中实体的知识图嵌入；我们表明，这些预处理的表示形式对各种下游建议和分类任务产生了显着的离线和在线改进：个性化的广告排名，账户遵循责任感，反对性内容，进攻性内容检测和搜索排名。我们讨论了部署行业规模HIN嵌入的设计选择和实践挑战，包括压缩它们以减少端到端的模型延迟和处理参数跨版本的处理。
Social networks, such as Twitter, form a heterogeneous information network (HIN) where nodes represent domain entities (e.g., user, content, advertiser, etc.) and edges represent one of many entity interactions (e.g, a user re-sharing content or "following" another). Interactions from multiple relation types can encode valuable information about social network entities not fully captured by a single relation; for instance, a user's preference for accounts to follow may depend on both user-content engagement interactions and the other users they follow. In this work, we investigate knowledge-graph embeddings for entities in the Twitter HIN (TwHIN); we show that these pretrained representations yield significant offline and online improvement for a diverse range of downstream recommendation and classification tasks: personalized ads rankings, account follow-recommendation, offensive content detection, and search ranking. We discuss design choices and practical challenges of deploying industry-scale HIN embeddings, including compressing them to reduce end-to-end model latency and handling parameter drift across versions.
