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LinkSAGE: Optimizing Job Matching Using Graph Neural Networks (2402.13430v1)

Published 20 Feb 2024 in cs.LG, cs.AI, and cs.SI

Abstract: 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.

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References (36)
  1. Meta-graph: Few shot link prediction via meta learning. arXiv preprint arXiv:1912.09867 (2019).
  2. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2023), 1–39.
  3. Adaptive Universal Generalized PageRank Graph Neural Network. In International Conference on Learning Representations. https://openreview.net/forum?id=n6jl7fLxrP
  4. Corné De Ruijt and Sandjai Bhulai. 2021. Job recommender systems: A review. arXiv preprint arXiv:2111.13576 (2021).
  5. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems 29 (2016).
  6. Eta prediction with graph neural networks in google maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 3767–3776.
  7. Graph transformation policy network for chemical reaction prediction. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 750–760.
  8. Twhin: Embedding the twitter heterogeneous information network for personalized recommendation. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2842–2850.
  9. Hypergraph neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 3558–3565.
  10. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
  11. Protein interface prediction using graph convolutional networks. Advances in neural information processing systems 30 (2017).
  12. Towards long-term fairness in recommendation. In Proceedings of the 14th ACM international conference on web search and data mining. 445–453.
  13. Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems. 60–68.
  14. Inductive representation learning on large graphs. Advances in neural information processing systems 30 (2017).
  15. A hybrid approach to managing job offers and candidates. Information processing & management 48, 6 (2012), 1124–1135.
  16. Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv:1609.02907 (2016).
  17. Towards effective and interpretable person-job fitting. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1883–1892.
  18. Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1–26.
  19. Exploiting job transition patterns for effective job recommendation. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2414–2419.
  20. Personalized fairness-aware re-ranking for microlending. In Proceedings of the 13th ACM Conference on Recommender Systems. 467–471.
  21. A novel approach for learning how to automatically match job offers and candidate profiles. Information Systems Frontiers 22 (2020), 1265–1274.
  22. Motebang Daniel Mpela and Tranos Zuva. 2020. A mobile proximity job employment recommender system. In 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 1–6.
  23. Job recommendation through progression of job selection. In 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 212–216.
  24. Enhancing person-job fit for talent recruitment: An ability-aware neural network approach. In The 41st international ACM SIGIR conference on research & development in information retrieval. 25–34.
  25. A note on explicit versus implicit information for job recommendation. Decision Support Systems 98 (2017), 26–35.
  26. Hybrid job offer recommender system in a social network. Expert Systems 36, 4 (2019), e12416.
  27. Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning.
  28. Alex Samylkin. 2022. DeepGNN is a framework for training machine learning models on large scale graph data. https://github.com/microsoft/DeepGNN
  29. Victor Garcia Satorras and Joan Bruna Estrach. 2018. Few-shot learning with graph neural networks. In International conference on learning representations.
  30. Graph Attention Networks. In International Conference on Learning Representations.
  31. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019).
  32. A Survey on the Fairness of Recommender Systems. ACM Trans. Inf. Syst. 41, 3, Article 52 (feb 2023), 43 pages. https://doi.org/10.1145/3547333
  33. Graph convolutional networks with markov random field reasoning for social spammer detection. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 1054–1061.
  34. Simple and efficient heterogeneous graph neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 10816–10824.
  35. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 974–983.
  36. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 793–803.
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