Embedding Based Retrieval (EBR) is a crucial component of the retrieval stage in (Ads) Recommendation System that utilizes Two Tower or Siamese Networks to learn embeddings for both users and items (ads). It then employs an Approximate Nearest Neighbor Search (ANN) to efficiently retrieve the most relevant ads for a specific user. Despite the recent rise to popularity in the industry, they have a couple of limitations. Firstly, Two Tower model architecture uses a single dot product interaction which despite their efficiency fail to capture the data distribution in practice. Secondly, the centroid representation and cluster assignment, which are components of ANN, occur after the training process has been completed. As a result, they do not take into account the optimization criteria used for retrieval model. In this paper, we present Hierarchical Structured Neural Network (HSNN), a deployed jointly optimized hierarchical clustering and neural network model that can take advantage of sophisticated interactions and model architectures that are more common in the ranking stages while maintaining a sub-linear inference cost. We achieve 6.5% improvement in offline evaluation and also demonstrate 1.22% online gains through A/B experiments. HSNN has been successfully deployed into the Ads Recommendation system and is currently handling major portion of the traffic. The paper shares our experience in developing this system, dealing with challenges like freshness, volatility, cold start recommendations, cluster collapse and lessons deploying the model in a large scale retrieval production system.

翻译：基于嵌入的检索（EBR）是（广告）推荐系统检索阶段的关键组件，它利用双塔或孪生网络学习用户和物品（广告）的嵌入表示，随后通过近似最近邻搜索（ANN）高效地为特定用户检索最相关的广告。尽管该技术近年来在工业界日益普及，但仍存在若干局限性：首先，双塔模型架构仅使用单点积交互，虽然高效却难以在实践中捕捉真实数据分布；其次，ANN中的质心表示与聚类分配环节在训练完成后才进行，导致其未能充分考虑检索模型的优化目标。本文提出层次结构化神经网络（HSNN），这是一种已部署的联合优化层次聚类与神经网络模型，能够在保持亚线性推理成本的同时，利用排序阶段更常见的复杂交互机制与模型架构。离线评估显示该模型性能提升6.5%，A/B实验亦验证其在线指标获得1.22%的增益。HSNN已成功部署于广告推荐系统，目前承担着主要流量处理任务。本文分享了系统开发经验，包括应对数据新鲜度、波动性、冷启动推荐、聚类坍缩等挑战的解决方案，以及在大规模检索生产系统中部署该模型的经验教训。