Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)

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2017. RecSys Challenge ’17: Proceedings of the Recommender Systems Challenge 2017 (Como, Italy). Association for Computing Machinery, New York, NY, USA.

Mathieu Bastian, Matthew Hayes, William Vaughan, Sam Shah, Peter Skomoroch, Hyungjin Kim, Sal Uryasev, and Christopher Lloyd. 2014. LinkedIn Skills: Large-Scale Topic Extraction and Inference. In Proceedings of the 8th ACM Conference on Recommender Systems(RecSys ’14). Association for Computing Machinery, New York, NY, USA, 1–8. https://doi.org/10.1145/2645710.2645729

Robert Charlier and Sander Kloppenburg. 2017. Artificial Intelligence in HR: a No-brainer.

Ketki V. Deshpande, Shimei Pan, and James R. Foulds. 2020. Mitigating Demographic Bias in AI-Based Resume Filtering. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (Genoa, Italy) (UMAP ’20 Adjunct). Association for Computing Machinery, New York, NY, USA, 268–275. https://doi.org/10.1145/3386392.3399569

Snorre S. Frid-Nielsen. 2019. Find my next job: labor market recommendations using administrative big data. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 408–412. https://doi.org/10.1145/3298689.3346992

Joseph B Fuller, Manjari Raman, Eva Sage-Gavin, and Kristen Hines. 2021. Hidden workers: Untapped talent. Harvard Business School Project on Managing the Future of Work and Accenture (2021).

David Graus, Toine Bogers, Mesut Kaya, Francisco Gutiérrez, and Katrien Verbert. 2022. Report on the 1st Workshop on Recommender Systems for Human Resources (RecSys in HR 2021) at RecSys 2021. SIGIR Forum 55, 2, Article 18 (mar 2022), 14 pages. https://doi.org/10.1145/3527546.3527567

Francisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, and Katrien Verbert. 2019. Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 60–68. https://doi.org/10.1145/3298689.3347001

Matissa Hollister. 2021. Human-Centred Artificial Intelligence for Human Resources.

Krishnaram Kenthapadi, Benjamin Le, and Ganesh Venkataraman. 2017. Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys ’17). Association for Computing Machinery, New York, NY, USA, 346–347. https://doi.org/10.1145/3109859.3109921

Akiva Kleinerman, Ariel Rosenfeld, and Sarit Kraus. 2018. Providing Explanations for Recommendations in Reciprocal Environments. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 22–30. https://doi.org/10.1145/3240323.3240362

Alina Köchling and Marius Claus Wehner. 2020. Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research 13, 3 (2020), 795–848. https://doi.org/10.1007/s40685-020-00134-w

Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, and Elisabeth Lex. 2019. Should We Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 496–500. https://doi.org/10.1145/3298689.3346989

Cynthia C. S. Liem, Markus Langer, Andrew Demetriou, Annemarie M. F. Hiemstra, Achmadnoer Sukma Wicaksana, Marise Ph. Born, and Cornelius J. König. 2018. Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening. Springer International Publishing, Cham, 197–253. https://doi.org/10.1007/978-3-319-98131-4_9

Dena F Mujtaba and Nihar R Mahapatra. 2019. Ethical considerations in AI-Based recruitment. In 2019 IEEE International Symposium on Technology and Society (ISTAS). IEEE, 1–7.

I. Naim, M. I. Tanveer, D. Gildea, and M. E. Hoque. 2018. Automated Analysis and Prediction of Job Interview Performance. IEEE Transactions on Affective Computing 9, 2 (2018), 191–204. https://doi.org/10.1109/TAFFC.2016.2614299

Ioannis Paparrizos, B. Barla Cambazoglu, and Aristides Gionis. 2011. Machine Learned Job Recommendation. In Proceedings of the Fifth ACM Conference on Recommender Systems(RecSys ’11). Association for Computing Machinery, New York, NY, USA, 325–328. https://doi.org/10.1145/2043932.2043994

Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. 2020. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 469–481. https://doi.org/10.1145/3351095.3372828

Amar Saini, Florin Rusu, and Andrew Johnston. 2019. PrivateJobMatch: a privacy-oriented deferred multi-match recommender system for stable employment. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, Toine Bogers, Alan Said, Peter Brusilovsky, and Domonkos Tikk (Eds.). ACM, 87–95. https://doi.org/10.1145/3298689.3346983

Javier Sánchez-Monedero, Lina Dencik, and Lilian Edwards. 2020. What does it mean to ’solve’ the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems. In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020, Mireille Hildebrandt, Carlos Castillo, Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 458–468. https://doi.org/10.1145/3351095.3372849

K. Umemoto, T. Milo, and M. Kitsuregawa. 2020. Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). 169–180. https://doi.org/10.1109/ICDE48307.2020.00022

Christo Wilson, Avijit Ghosh, Shan Jiang, Alan Mislove, Lewis Baker, Janelle Szary, Kelly Trindel, and Frida Polli. 2021. Building and Auditing Fair Algorithms: A Case Study in Candidate Screening. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 666–677. https://doi.org/10.1145/3442188.3445928

F. Zuiderveen Borgesius. 2018. Discrimination, artificial intelligence, and algorithmic decision-making.