LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
Ping Liu Haichao Wei Xiaochen Hou Jianqiang Shen Shihai He Kay Qianqi Shen Zhujun Chen Fedor Borisyuk Daniel Hewlett Liang Wu Srikant Veeraraghavan Alex Tsun Chengming Jiang Wenjing Zhang
We present LinkSAGE, an innovative framework that integrates Graph Neural
Networks (GNNs) into large-scale personalized job matching systems, designed to
address the complex dynamics of LinkedIns extensive professional network. Our
approach capitalizes on a novel job marketplace graph, the largest and most
intricate of its kind in industry, with billions of nodes and edges. This graph
is not merely extensive but also richly detailed, encompassing member and job
nodes along with key attributes, thus creating an expansive and interwoven
network. A key innovation in LinkSAGE is its training and serving methodology,
which effectively combines inductive graph learning on a heterogeneous,
evolving graph with an encoder-decoder GNN model. This methodology decouples
the training of the GNN model from that of existing Deep Neural Nets (DNN)
models, eliminating the need for frequent GNN retraining while maintaining
up-to-date graph signals in near realtime, allowing for the effective
integration of GNN insights through transfer learning. The subsequent nearline
inference system serves the GNN encoder within a real-world setting,
significantly reducing online latency and obviating the need for costly
real-time GNN infrastructure. Validated across multiple online A/B tests in
diverse product scenarios, LinkSAGE demonstrates marked improvements in member
engagement, relevance matching, and member retention, confirming its
generalizability and practical impact.