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
• License:
• 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.

Related papers

• Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees [50.78679002846741]
  We introduce a novel approach for learning cross-task generalities in graphs. We propose task-trees as basic learning instances to align task spaces on graphs. Our findings indicate that when a graph neural network is pretrained on diverse task-trees, it acquires transferable knowledge.
  arXiv  Detail & Related papers  (2024-12-21T02:07:43Z)
• Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks [25.720233631885726]
  integration of Graph Neural Networks (GNNs) and Large Language Models (LLMs) has emerged as a promising technological paradigm. We leverage graph description texts with rich semantic context to fundamentally enhance Data quality. This work serves as a foundational reference for researchers and practitioners looking to advance graph learning methodologies.
  arXiv  Detail & Related papers  (2024-12-17T01:41:17Z)
• Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs [14.392577069212292]
  We introduce SCORE, a unified graph reasoning framework that effectively generalizes diverse graph tasks using zero-shot learning. We evaluate SCORE using 38 diverse graph datasets, covering node-level, link-level, and graph-level tasks across multiple domains.
  arXiv  Detail & Related papers  (2024-10-16T14:26:08Z)
• Can Graph Learning Improve Planning in LLM-based Agents? [61.47027387839096]
  Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs) In this paper, we explore graph learning-based methods for task planning, a direction that is to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs.
  arXiv  Detail & Related papers  (2024-05-29T14:26:24Z)
• Professional Network Matters: Connections Empower Person-Job Fit [62.20651880558674]
  This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn.
  arXiv  Detail & Related papers  (2023-12-19T06:44:44Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction [0.0]
  We introduce a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types. We show that further pre-training the language models with this additional embedding layer using the triples extracted from the knowledge graph, followed by the standard fine-tuning phase sets a new state-of-the-art performance for the link prediction task on the benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-04T20:38:12Z)
• Mutual Graph Learning for Camouflaged Object Detection [31.422775969808434]
  A major challenge is that intrinsic similarities between foreground objects and background surroundings make the features extracted by deep model indistinguishable. We design a novel Mutual Graph Learning model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. In contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations.
  arXiv  Detail & Related papers  (2021-04-03T10:14:39Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.