research-article

Large-Scale Talent Flow Embedding for Company Competitive Analysis

Authors:

Enhong ChenAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2354 - 2364

https://doi.org/10.1145/3366423.3380299

Published: 20 April 2020 Publication History

Abstract

Recent years have witnessed the growing interests in investigating the competition among companies. Existing studies for company competitive analysis generally rely on subjective survey data and inferential analysis. Instead, in this paper, we aim to develop a new paradigm for studying the competition among companies through the analysis of talent flows. The rationale behind this is that the competition among companies usually leads to talent movement. Along this line, we first build a Talent Flow Network based on the large-scale job transition records of talents, and formulate the concept of “competitiveness” for companies with consideration of their bi-directional talent flows in the network. Then, we propose a Talent Flow Embedding (TFE) model to learn the bi-directional talent attractions of each company, which can be leveraged for measuring the pairwise competitive relationships between companies. Specifically, we employ the random-walk based model in original and transpose networks respectively to learn representations of companies by preserving their competitiveness. Furthermore, we design a multi-task strategy to refine the learning results from a fine-grained perspective, which can jointly embed multiple talent flow networks by assuming the features of company keep stable but take different roles in networks of different job positions. Finally, extensive experiments on a large-scale real-world dataset clearly validate the effectiveness of our TFE model in terms of company competitive analysis and reveal some interesting rules of competition based on the derived insights on talent flows.

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Cited By

Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3651158
Zhu HGe YXiong HLim EBaeza-Yates RBonchi F(2024)The 5th International Workshop on Talent and Management Computing (TMC'2024)Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671479(6759-6760)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671479
Yamashita MTran TLee DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fake Resume Attacks: Data Poisoning on Online Job PlatformsProceedings of the ACM Web Conference 202410.1145/3589334.3645524(1734-1745)Online publication date: 13-May-2024
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Index Terms

Large-Scale Talent Flow Embedding for Company Competitive Analysis
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

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Cited By

Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3651158
Zhu HGe YXiong HLim EBaeza-Yates RBonchi F(2024)The 5th International Workshop on Talent and Management Computing (TMC'2024)Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671479(6759-6760)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671479
Yamashita MTran TLee DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fake Resume Attacks: Data Poisoning on Online Job PlatformsProceedings of the ACM Web Conference 202410.1145/3589334.3645524(1734-1745)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645524
Chen LQin CSun YSong XXu TZhu HXiong HChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Collaboration-Aware Hybrid Learning for Knowledge Development PredictionProceedings of the ACM Web Conference 202410.1145/3589334.3645326(3976-3985)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645326
Ye YZhu HCui TYu RZhang LXiong H(2024)University Evaluation Through Graduate Employment Prediction: An Influence Based Graph Autoencoder ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340223436:11(7255-7267)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3402234
Yang BZhang HFan FHuang R(2024)Mapping the structural evolution of intercity patent transfer constrained by space: a case study of ChinaTechnology Analysis & Strategic Management10.1080/09537325.2024.2369557(1-18)Online publication date: 8-Jul-2024
https://doi.org/10.1080/09537325.2024.2369557
Liu XWang YDong QLu X(2024)Job Title Prediction as a Dual Task of Expertise Prediction in Open Source SoftwareMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_24(381-396)Online publication date: 22-Aug-2024
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Ye XSun YLiu DLi T(2023)A Multisource Data Fusion-based Heterogeneous Graph Attention Network for Competitor PredictionACM Transactions on Knowledge Discovery from Data10.1145/362510118:2(1-20)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3625101
Qin CZhu HShen DSun YYao KWang PXiong H(2023)Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job InterviewsACM Transactions on Information Systems10.1145/360455242:1(1-32)Online publication date: 13-Jun-2023
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Zhu HXiong HGe YLim ESingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)The 4th International Workshop on Talent and Management Computing (TMC'2023)Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599200(5909-5910)Online publication date: 6-Aug-2023
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