abstract

Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned

Authors:

Krishnaram Kenthapadi,

Benjamin Le,

Ganesh VenkataramanAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 346 - 347

https://doi.org/10.1145/3109859.3109921

Published: 27 August 2017 Publication History

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Abstract

Online professional social networks such as LinkedIn play a key role in helping job seekers find right career opportunities and job providers reach out to potential candidates. LinkedIn's job ecosystem has been designed to serve as a marketplace for efficient matching between potential candidates and job postings, and to provide tools to connect job seekers and job providers. LinkedIn's job recommendations product is a crucial mechanism to help achieve these goals, wherein personalized sets of recommended job postings are presented for members based on the structured, context data present in their profiles.

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Cited By

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Mueller VSchaaff K(2025)AI-Based Recommender System for Employee-Project Matching of IT SpecialistsArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-97-9255-9_20(296-311)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-981-97-9255-9_20
Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
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Index Terms

Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

Information

Published In

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Conference

RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

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

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Cited By

View all

Mueller VSchaaff K(2025)AI-Based Recommender System for Employee-Project Matching of IT SpecialistsArtificial Intelligence in Education Technologies: New Development and Innovative Practices10.1007/978-981-97-9255-9_20(296-311)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-981-97-9255-9_20
Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Bogers TGraus DKaya MJohnson CDecorte JDe Bie T(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
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Shen JJuan YLiu PPu WZhang SShen QHong LZhang WSerra ESpezzano F(2024)Learning Links for Adaptable and Explainable RetrievalProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679953(4046-4050)Online publication date: 21-Oct-2024
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Zhang ZZhang MYu YYang CLiu JShi CChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Endowing Pre-trained Graph Models with Provable FairnessProceedings of the ACM Web Conference 202410.1145/3589334.3645703(1045-1056)Online publication date: 13-May-2024
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Nowling R(2024)Experience Report from a Graduate ML Production Systems Course2024 IEEE International Conference on Electro Information Technology (eIT)10.1109/eIT60633.2024.10609876(058-065)Online publication date: 30-May-2024
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Kamran SHosseini SEsmailzadeh SKangavari MHua W(2024)Cognition2Vocation: meta-learning via ConvNets and continuous transformersNeural Computing and Applications10.1007/s00521-024-09749-036:21(12935-12950)Online publication date: 23-Apr-2024
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Aziz MMahmud TUddin MHossain SDatta NAkther SHossain MAndersson K(2024)Machine Learning-Driven Job Recommendations: Harnessing Genetic AlgorithmsProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-3305-7_38(471-480)Online publication date: 30-Jul-2024
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