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Machine learned job recommendation

Published: 23 October 2011 Publication History

Abstract

We address the problem of recommending suitable jobs to people who are seeking a new job. We formulate this recommendation problem as a supervised machine learning problem. Our technique exploits all past job transitions as well as the data associated with employees and institutions to predict an employee's next job transition. We train a machine learning model using a large number of job transitions extracted from the publicly available employee profiles in the Web. Experiments show that job transitions can be accurately predicted, significantly improving over a baseline that always predicts the most frequent institution in the data.

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A. Calvó-Armengol and Y. Zenou. Job matching, social network and word-of-mouth communication. Technical Report 771, IZA, 2003.
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D. Gale and L. S. Shapley. College admissions and the stability of marriage. American Mathematical Monthly, 69(1), 1962.
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A. Galeotti and L. P. Merlino. Endogenous job contact networks, 2010.
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M. S. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6), 1973.
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M. Hall and E. Frank. Combining naive Bayes and decision tables. In FLAIRS, 2008.
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R. Irving. Matching medical students to pairs of hospitals: a new variation on a well-known theme. In ESA, 1998.
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J. Malinowski, T. Keim, O. Wendt, and T. Weitzel. Matching people and jobs: a bilateral recommendation approach. In HICSS, 2006.
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A. E. Roth. The economics of matching: stability and incentives. Mathematics of Operations Research, 1982.
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C. Wei and I.-T. Chiu. Turning telecommunications call details to churn prediction: a data mining approach. Expert Systems with Applications, 23, 2002.

Cited By

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  • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
  • (2024)Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687109(1222-1226)Online publication date: 8-Oct-2024
  • (2024)Unlocking Potential: A Machine Learning Approach to Job Category Prediction2024 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP61132.2024.10752119(1-6)Online publication date: 27-Sep-2024
  • Show More Cited By

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    cover image ACM Conferences
    RecSys '11: Proceedings of the fifth ACM conference on Recommender systems
    October 2011
    414 pages
    ISBN:9781450306836
    DOI:10.1145/2043932
    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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    New York, NY, United States

    Publication History

    Published: 23 October 2011

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    Author Tags

    1. employee
    2. institution
    3. job recommendation
    4. job transition

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    RecSys '11
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    RecSys '11: Fifth ACM Conference on Recommender Systems
    October 23 - 27, 2011
    Illinois, Chicago, USA

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    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
    • (2024)Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687109(1222-1226)Online publication date: 8-Oct-2024
    • (2024)Unlocking Potential: A Machine Learning Approach to Job Category Prediction2024 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP61132.2024.10752119(1-6)Online publication date: 27-Sep-2024
    • (2024)Resume Parser and Job Recommendation System using Machine Learning2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)10.1109/ESIC60604.2024.10481635(157-162)Online publication date: 9-Feb-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)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)AI-Based Resume Matching and PredictionSmart Trends in Computing and Communications10.1007/978-981-97-1323-3_31(371-383)Online publication date: 26-May-2024
    • (2023)Estimating propensity for causality-based recommendation without exposure dataProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668373(51688-51705)Online publication date: 10-Dec-2023
    • (2023)Strategic behavior in two-sided matching markets with recommendation-enhanced preference-formationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667685(36041-36052)Online publication date: 10-Dec-2023
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