{"type":"ScholarlyArticle","title":"Pre-DyGAE: Pre-training Enhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting","authors":[{"name":"Xi Chen","slug":"xi-chen","given":"Xi","family":"Chen","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Career Science Lab , BOSS Zhipin"},{"name":"Chuan Qin","slug":"chuan-qin","given":"Chuan","family":"Qin","affiliation":"Career Science Lab , BOSS Zhipin; PBC School of Finance , Tsinghua University"},{"name":"Zhigaoyuan Wang","slug":"zhigaoyuan-wang","given":"Zhigaoyuan","family":"Wang","affiliation":"Artificial Intelligence Thrust , The Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Yihang Cheng","slug":"yihang-cheng","given":"Yihang","family":"Cheng","affiliation":"Career Science Lab , BOSS Zhipin"},{"name":"Chao Wang","slug":"chao-wang","given":"Chao","family":"Wang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou , China"},{"name":"Hengshu Zhu","slug":"hengshu-zhu","given":"Hengshu","family":"Zhu","affiliation":"Career Science Lab , BOSS Zhipin"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Artificial Intelligence Thrust , The Hong Kong University of Science and Technology (Guangzhou )"}],"abstract":"Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific to occupations, beneficial for employees and employers to grasp occupational nature and maintain a competitive edge in the rapidly evolving labor market. Although recent research has proposed data-driven techniques for forecasting skill demand, the focus has remained predominantly on overall trends rather than occupational granularity. In this paper, we propose a novel Pre-training Enhanced Dynamic Graph Autoencoder (Pre-DyGAE), forecasting skill demand from an occupational perspective. Specifically, we aggregate job descriptions (JDs) by occupation and segment them into several timestamps. Subsequently, in the initial timestamps, we pre-train a graph autoencoder (GAE), consisting of a semantically-aware cross-attention enhanced uncertainty-aware encoder and decoders for link prediction and edge regression to achieve graph reconstruction. In particular, we utilize contrastive learning on skill cooccurrence clusters to solve the data sparsity and a unified Tweedie and ranking loss for predicting the imbalanced distribution. Afterward, we incorporate an adaptive temporal encoding unit and a temporal shift module into GAE to achieve a dynamic GAE (DyGAE). Furthermore, we fine-tune the DyGAE with a two-stage optimization strategy and infer future representations. Extensive experiments on four real-world datasets validate the effectiveness of Pre-DyGAE compared with state-of-the-art baselines.","doi":"10.24963/ijcai.2024/222","url":"https://ijcai.org/proceedings/2024/222","pdf":"https://ijcai.org/proceedings/2024/0222.pdf","markdown":"https://ijcai.org/proceedings/2024/222.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/222","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":222,"pages":{"first":2009,"last":2017},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2020] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 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