Graph embedding based retrieval has become one of the most popular techniques
in the information retrieval community and search engine industry. The
classical paradigm mainly relies on the flat Euclidean geometry. In recent
years, hyperbolic (negative curvature) and spherical (positive curvature)
representation methods have shown their superiority to capture hierarchical and
cyclic data structures respectively. However, in industrial scenarios such as
e-commerce sponsored search platforms, the large-scale heterogeneous
query-item-advertisement interaction graphs often have multiple structures
coexisting. Existing methods either only consider a single geometry space, or
combine several spaces manually, which are incapable and inflexible to model
the complexity and heterogeneity in the real scenario. To tackle this
challenge, we present a web-scale Adaptive Mixed-Curvature ADvertisement
retrieval system (AMCAD) to automatically capture the complex and heterogeneous
graph structures in non-Euclidean spaces. Specifically, entities are
represented in adaptive mixed-curvature spaces, where the types and curvatures
of the subspaces are trained to be optimal combinations. Besides, an attentive
edge-wise space projector is designed to model the similarities between
heterogeneous nodes according to local graph structures and the relation types.
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms
with hundreds of million users, we design an efficient two-layer online
retrieval framework for the task of graph based advertisement retrieval.
Extensive evaluations on real-world datasets and A/B tests on online traffic
are conducted to illustrate the effectiveness of the proposed system.

提出了一个自适应混合曲率广告检索系统（AMCAD），可以自动捕获非欧几里德（Euclidean）空间中的复杂和异构图结构，采用自适应混合曲率空间表示实体，并设计了注意力机制来模拟异构节点之间的相似度，设计了一个高效的二层在线检索框架来部署 AMCAD。