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Correcting for Recency Bias in Job Recommendation

Published: 03 November 2019 Publication History

Abstract

Users are known to interact more with fresh content in certain temporally associated domains such as news search or job seeking, leading to an uneven distribution of interactions over items of different degrees of freshness. Data collected under such an "aging effect'' is usually used unconditionally on all sort of recommendation tasks, and as a result more recently published content may be over-represented during model training and evaluation. In this study, we characterize this temporal influence as a recency bias, and present an analysis in the domain of job recommendation. We show that, by correcting for recency bias using an unbiased learning to rank approach, one can improve the quality of recommendation significantly over a recent neural collaborative filtering model on RecSys Challenge 2017 data.

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  • (2024)Fairness and Bias in Algorithmic Hiring: A Multidisciplinary SurveyACM Transactions on Intelligent Systems and Technology10.1145/369645716:1(1-54)Online publication date: 23-Sep-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 '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
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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Published: 03 November 2019

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Author Tags

  1. job recommendation
  2. recency bias

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2024)A bias study and an unbiased deep neural network for recommender systemsWeb Intelligence10.3233/WEB-23003622:1(15-29)Online publication date: 26-Mar-2024
  • (2024)Fairness and Bias in Algorithmic Hiring: A Multidisciplinary SurveyACM Transactions on Intelligent Systems and Technology10.1145/369645716:1(1-54)Online publication date: 23-Sep-2024
  • (2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
  • (2023)Recent Advancements in Unbiased Learning to RankProceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3632754.3632942(145-148)Online publication date: 15-Dec-2023
  • (2023)Multi-Behavior Job Recommendation with Dynamic AvailabilityProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625314(264-271)Online publication date: 26-Nov-2023
  • (2023)Recent Advances in the Foundations and Applications of Unbiased Learning to RankProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594247(3440-3443)Online publication date: 19-Jul-2023
  • (2023)Implications and New Directions for IR Research and PracticesA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_7(181-201)Online publication date: 18-Feb-2023
  • (2021)Biases in Recommendation SystemProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3473897(855-859)Online publication date: 13-Sep-2021
  • (2021)ULTRA: An Unbiased Learning To Rank Algorithm ToolboxProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482020(4613-4622)Online publication date: 26-Oct-2021
  • (2020)Keeping Dataset Biases out of the SimulationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412252(190-199)Online publication date: 22-Sep-2020

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