#10: Recommender Systems in Human Resources with David Graus
11/16/22 • 63 min
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.
Links from the Episode:
- David Graus on LinkedIn
- David Graus on Twitter
- David's Website
- RecSys in HR 2022: Workshop on Recommender Systems for Human Recources
- Randstad Annual Report 2021
- Talk by David Graus at Anti-Discrimination Hackaton on "Algorithmic matching, bias, and bias mitigation"
Papers:
- Arafan et al. (2022): End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates
- Geyik et al. (2019): Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
General Links:
- Follow me on Twitter: https://twitter.com/LivesInAnalogia
- Send me your comments, questions and suggestions to [email protected]
- 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
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.
Links from the Episode:
- David Graus on LinkedIn
- David Graus on Twitter
- David's Website
- RecSys in HR 2022: Workshop on Recommender Systems for Human Recources
- Randstad Annual Report 2021
- Talk by David Graus at Anti-Discrimination Hackaton on "Algorithmic matching, bias, and bias mitigation"
Papers:
- Arafan et al. (2022): End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates
- Geyik et al. (2019): Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
General Links:
- Follow me on Twitter: https://twitter.com/LivesInAnalogia
- Send me your comments, questions and suggestions to [email protected]
- 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
Previous Episode
#9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin Verachtert
In episode number nine of Recsperts we talk with the creators of RecPack which is a new Python package for recommender systems. We discuss how Froomle provides modularized personalization for customers in the news and e-commerce sectors. I talk to Lien Michiels and Robin Verachtert who are both industrial PhD students at the University of Antwerp and who work for Froomle. We also hear about their research on filter bubbles as well as model drift along with their RecSys 2022 contributions.
In this episode we introduce RecPack as a new recommender package that is easy to use and to extend and which allows for consistent experimentation. Lien and Robin share with us how RecPack evolved, its structure as well as the problems in research and practice they intend to solve with their open source contribution.
My guests also share many insights from their work at Froomle where they focus on modularized personalization with more than 60 recommendation scenarios and how they integrate these with their customers. We touch on topics like model drift and the need for frequent retraining as well as on the tradeoffs between accuracy, cost, and timeliness in production recommender systems.
In the end we also exchange about Lien's critical reception of using the term 'filter bubble', an operationalized definition of them as well as Robin's research on model degradation and training data selection.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Links from the Episode:
- Lien Michiels on LinkedIn
- Lien Michiels on Twitter
- Robin Verachtert on LinkedIn
- RecPack on GitLab
- RecPack Documentation
- FROOMLE
- PERSPECTIVES 2022: Perspectives on the Evaluation of Recommender Systems
- PERSPECTIVES 2022: Preview on "Towards a Broader Perspective in Recommender Evaluation" by Benedikt Loepp
- 5th FAccTRec Workshop: Responsible Recommendation
Papers:
- Verachtert et al. (2022): Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window
- Leysen and Michiels et al. (2022): What Are Filter Bubbles Really? A Review of the Conceptual and Empirical Work
- Michiels and Verachtert et al. (2022): RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data
- Dahlgren (2021): A critical review of filter bubbles and a comparison with selective exposure
General Links:
- Follow me on Twitter: https://twitter.com/LivesInAnalogia
- Send me your comments, questions and suggestions to [email protected]
- Podcast Website: https://www.recsperts.com/
- (03:23) - Introduction Lien Michiels
- (07:01) - Introduction Robin Verachtert
- (09:29) - RecPack - Python Recommender Package
- (52:31) - Modularized Personalization in News and E-commerce by Froomle
- (01:09:54) - Research on Model Drift and Filter Bubbles
- (01:18:07) - Closing Questions
Next Episode
#11: Personalized Advertising, Economic and Generative Recommenders with Flavian Vasile
In this episode of Recsperts we talk to Flavian Vasile about the work of his team at Criteo AI Lab on personalized advertising. We learn about the different stakeholders like advertisers, publishers, and users and the role of recommender systems in this marketplace environment. We learn more about the pros and cons of click versus conversion optimization and transition to econ(omic) reco(mmendations), a new approach to model the effect of a recommendations system on the users' decision making process. Economic theory plays an important role for this conceptual shift towards better recommender systems.
In addition, we discuss generative recommenders as an approach to directly translate a user’s preference model into a textual and/or visual product recommendation. This can be used to spark product innovation and to potentially generate what users really want. Besides that, it also allows to provide recommendations from the existing item corpus.
In the end, we catch up on additional real-world challenges like two-tower models and diversity in recommendations.
Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Chapters:
- (02:37) - Introduction Flavian Vasile
- (06:46) - Personalized Advertising at Criteo
- (18:29) - Moving from Click to Conversion optimization
- (23:04) - Econ(omic) Reco(mmendations)
- (41:56) - Generative Recommender Systems
- (01:04:03) - Additional Real-World Challenges in RecSys
- (01:08:00) - Final Remarks
Links from the Episode:
- Flavian Vasile on LinkedIn
- Flavian Vasile on Twitter
- Modern Recommendation for Advanced Practitioners - Part I (2019)
- Modern Recommendation for Advanced Practitioners - Part II (2019)
- CONSEQUENCES+REVEAL Workshop at RecSys 2022: Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning for Recommender Systems
Papers:
- Heymann et al. (2022): Welfare-Optimized Recommender Systems
- Samaran et al. (2021): What Users Want? WARHOL: A Generative Model for Recommendation
- Bonner et al (2018): Causal Embeddings for Recommendation
- Vasile et al. (2016): Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation
General Links:
- Follow me on Twitter: https://twitter.com/LivesInAnalogia
- Send me your comments, questions and suggestions to [email protected]
- Podcast Website: https://www.recsperts.com/
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