AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System

TL;DR

AMCAD introduces an adaptive mixed-curvature graph embedding system for large-scale e-commerce advertisement retrieval, effectively modeling complex heterogeneous data structures with automatic curvature optimization and an efficient online retrieval framework.

Contribution

The paper proposes a novel adaptive mixed-curvature embedding method with an attentive space projector, enabling flexible modeling of heterogeneous graph structures in industrial-scale advertisement retrieval.

Findings

Outperforms existing methods on real-world datasets.

Effective in capturing complex graph heterogeneity.

Demonstrated success in large-scale online deployment.

Abstract

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.