#10: Recommender Systems in Human Resources with David Graus

Recsperts - Recommender Systems Experts

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2+ y ago 1:03:23

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In episode number ten of Recsperts I welcome David Graus who is the Data Science Chapter Lead at Randstad Groep Nederland, a global leader in providing Human Resource services. We talk about the role of recommender systems in the HR domain which includes vacancy recommendations for candidates, but also generating talent recommendations for recruiters at Randstad. We also learn which biases might have an influence when using recommenders for decision support in the recruiting process as well as how Randstad mitigates them.

In this episode we learn more about another domain where recommender systems can serve humans by effective decision support: Human Resources. Here, everything is about job recommendations, matching candidates with vacancies, but also exploiting knowledge about career path to propose learning opportunities and assist with career development. David Graus leads those efforts at Randstad and has previously worked in the news recommendation domain after obtaining his PhD from the University of Amsterdam.
We discuss the most recent contribution by Randstad on mitigating bias in candidate recommender systems by introducing fairness-oriented post- and preprocessing to a recommendation pipeline. We learn that one can maintain user satisfaction while improving fairness at the same time (demographic parity measuring gender balance in this case).

David and I also touch on his engagement in co-organizing the RecSys in HR workshops since RecSys 2021.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

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Podcast Website: https://www.recsperts.com/

(02:23) - Introduction David Graus
(13:55) - About Randstad and the Staffing Industry
(17:09) - Use Cases for RecSys Application in HR
(22:04) - Talent and Vacancy Recommender System
(33:46) - RecSys in HR Workshop
(38:48) - Fairness for RecSys in HR
(52:40) - Other HR RecSys Challenges
(56:40) - Further RecSys Challenges

29 episodes

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