LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
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arXiv
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866910338874605568 |
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| author | Liu, Ping Wei, Haichao Hou, Xiaochen Shen, Jianqiang He, Shihai Shen, Kay Qianqi Chen, Zhujun Borisyuk, Fedor Hewlett, Daniel Wu, Liang Veeraraghavan, Srikant Tsun, Alex Jiang, Chengming Zhang, Wenjing |
| author_facet | Liu, Ping Wei, Haichao Hou, Xiaochen Shen, Jianqiang He, Shihai Shen, Kay Qianqi Chen, Zhujun Borisyuk, Fedor Hewlett, Daniel Wu, Liang Veeraraghavan, Srikant Tsun, Alex Jiang, Chengming Zhang, Wenjing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LinkSAGE: Optimizing Job Matching Using Graph Neural Networks Liu, Ping Wei, Haichao Hou, Xiaochen Shen, Jianqiang He, Shihai Shen, Kay Qianqi Chen, Zhujun Borisyuk, Fedor Hewlett, Daniel Wu, Liang Veeraraghavan, Srikant Tsun, Alex Jiang, Chengming Zhang, Wenjing Machine Learning Artificial Intelligence Social and Information Networks 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. |
| title | LinkSAGE: Optimizing Job Matching Using Graph Neural Networks |
| topic | Machine Learning Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2402.13430 |