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A Combined Representation Learning Approach for Better Job and Skill Recommendation

Published: 17 October 2018 Publication History

Abstract

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to acquire few additional skills which are required to assume the new position. In this work, we created three types of information net- works from the historical job data: (i) job transition network, (ii) job-skill network, and (iii) skill co-occurrence network. We provide a representation learning model which can utilize the information from all three networks to jointly learn the representation of the jobs and skills in the shared k-dimensional latent space. In our experiments, we show that by jointly learning the representation for the jobs and skills, our model provides better recommendation for both jobs and skills. Additionally, we also show some case studies which validate our claims.

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  • (2024)Integration and Recommendation System of Profiles based on Professional Social NetworksEAI Endorsed Transactions on Context-aware Systems and Applications10.4108/eetcasa.450010:1Online publication date: 15-Jan-2024
  • (2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
  • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
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      cover image ACM Conferences
      CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
      October 2018
      2362 pages
      ISBN:9781450360142
      DOI:10.1145/3269206
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 17 October 2018

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      Author Tags

      1. job recommendation
      2. skill recommendation

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      CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
      Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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      Cited By

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      • (2024)Integration and Recommendation System of Profiles based on Professional Social NetworksEAI Endorsed Transactions on Context-aware Systems and Applications10.4108/eetcasa.450010:1Online publication date: 15-Jan-2024
      • (2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
      • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
      • (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
      • (2024)A Hypergraph-based Resume-Job Matching Model for Recommendation2024 IEEE 4th International Conference on Digital Twins and Parallel Intelligence (DTPI)10.1109/DTPI61353.2024.10778691(49-54)Online publication date: 18-Oct-2024
      • (2024)OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825519(6697-6706)Online publication date: 15-Dec-2024
      • (2024)Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825459(2334-2339)Online publication date: 15-Dec-2024
      • (2024)Job Seeker Recommendation for Employers: A Graph-Based Recommendation Approach Using Node EmbeddingProcedia Computer Science10.1016/j.procs.2023.10.361225:C(3660-3669)Online publication date: 4-Mar-2024
      • (2024)Leveraging multiple behaviors and explicit preferences for job recommendationExpert Systems with Applications10.1016/j.eswa.2024.125149258(125149)Online publication date: Dec-2024
      • (2024)Extracting section structure from resumes in Brazilian PortugueseExpert Systems with Applications10.1016/j.eswa.2023.122495242(122495)Online publication date: May-2024
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