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Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn

Published: 11 April 2016 Publication History
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  • Abstract

    One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typically needed for the position, what are their alternatives, which companies are likely to have such candidates, etc. However, listing examples of suitable candidates for a given position is a relatively easy job. Therefore, in order to help searchers overcome this challenge, we design a next generation of talent search paradigm at LinkedIn: Search by Ideal Candidates. This new system only needs the searcher to input one or several examples of suitable candidates for the position. The system will generate a query based on the input candidates and then retrieve and rank results based on the query as well as the input candidates. The query is also shown to the searcher to make the system transparent and to allow the searcher to interact with it. As the searcher modifies the initial query and makes it deviate from the ideal candidates, the search ranking function dynamically adjusts an refreshes the ranking results balancing between the roles of query and ideal candidates. As of writing this paper, the new system is being launched to our customers.

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    Cited By

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    • (2024)Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firmsReview of Accounting Studies10.1007/s11142-024-09821-zOnline publication date: 28-Mar-2024
    • (2022)Wisdom from the crowd: Can recommender systems predict employee turnover and its destinations?Personnel Psychology10.1111/peps.1255177:2(475-496)Online publication date: 13-Dec-2022
    • (2021)A Comprehensive Review of Professional Network Impact on Education and CareerChallenges and Applications of Data Analytics in Social Perspectives10.4018/978-1-7998-2566-1.ch001(1-26)Online publication date: 2021
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    1. Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn

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      Published In

      cover image ACM Other conferences
      WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web
      April 2016
      1094 pages
      ISBN:9781450341448
      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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      • IW3C2: International World Wide Web Conference Committee

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      International World Wide Web Conferences Steering Committee

      Republic and Canton of Geneva, Switzerland

      Publication History

      Published: 11 April 2016

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      Author Tags

      1. query by example
      2. query generation
      3. ranking
      4. talent search

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      • Demonstration

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      WWW '16
      Sponsor:
      • IW3C2
      WWW '16: 25th International World Wide Web Conference
      April 11 - 15, 2016
      Québec, Montréal, Canada

      Acceptance Rates

      WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;
      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      Cited By

      View all
      • (2024)Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firmsReview of Accounting Studies10.1007/s11142-024-09821-zOnline publication date: 28-Mar-2024
      • (2022)Wisdom from the crowd: Can recommender systems predict employee turnover and its destinations?Personnel Psychology10.1111/peps.1255177:2(475-496)Online publication date: 13-Dec-2022
      • (2021)A Comprehensive Review of Professional Network Impact on Education and CareerChallenges and Applications of Data Analytics in Social Perspectives10.4018/978-1-7998-2566-1.ch001(1-26)Online publication date: 2021
      • (2021)FINN: Feature Interaction Neural Network for Person-Job Fit2021 7th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA53151.2021.9619599(123-130)Online publication date: 29-Oct-2021
      • (2021)Security Professional Skills Representation in Bug Bounty Programs and ProcessesService-Oriented Computing – ICSOC 2020 Workshops10.1007/978-3-030-76352-7_33(334-348)Online publication date: 30-May-2021
      • (2020)Learning to Ask Screening Questions for Job PostingsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401118(549-558)Online publication date: 25-Jul-2020
      • (2020)An Efficient Approach for Job Recommendation System Based on Collaborative FilteringICT Systems and Sustainability10.1007/978-981-15-0936-0_16(169-176)Online publication date: 29-Feb-2020
      • (2019)Entity Personalized Talent Search Models with Tree Interaction FeaturesThe World Wide Web Conference10.1145/3308558.3313672(3116-3122)Online publication date: 13-May-2019
      • (2018)Towards Deep and Representation Learning for Talent Search at LinkedInProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272030(2253-2261)Online publication date: 17-Oct-2018
      • (2018)In-Session Personalization for Talent SearchProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272012(2107-2115)Online publication date: 17-Oct-2018
      • Show More Cited By

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