Understanding and Modeling Job Marketplace with Pretrained Language Models

• URL: http://arxiv.org/abs/2408.04381v1
• Date: Thu, 08 Aug 2024 11:35:52 GMT
• Title: Understanding and Modeling Job Marketplace with Pretrained Language Models
• Authors: Yaochen Zhu, Liang Wu, Binchi Zhang, Song Wang, Qi Guo, Liangjie Hong, Luke Simon, Jundong Li,
• Abstract summary: Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. We propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph.
• Score: 46.05972662117288
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• Abstract: Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job seekers and employers, ultimately contributing to the greater good of the society. However, existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. To address the above challenges, we propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph, aiming to fully utilize the pretrained knowledge and reasoning ability to model member/job textual features as well as various member-job relations simultaneously. In the pretraining phase, we propose a heterogeneous ego-graph-based prompting strategy to model and aggregate member/job textual features based on the topological structure around the target member/job node, where entity type embeddings and graph positional embeddings are introduced accordingly to model different entities and their heterogeneous relations. Meanwhile, a proximity-aware attention alignment strategy is designed to dynamically adjust the attention of the PLM on ego-graph node tokens in the prompt, such that the attention can be better aligned with job marketplace semantics. Extensive experiments at LinkedIn demonstrate the effectiveness of PLM4Job.

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