Very excited that this year too I will engage in one of my favorite “extra-curricular activities” 😅: our proposal to organize the (third!) “Recommender Systems in Human Resources” workshop has been accepted in the the program of the 17th ACM Conference on Recommender Systems!
Looking forward to creating an engaging program with our fantastic organizing committee consisting of Toine Bogers (IT University of Copenhagen), Mesut Kaya (Aalborg University Copenhagen), Chris Johnson (Indeed.com), and welcoming Jens-Joris Decorte (of TechWolf) on board this year!
👉 I was happy to (virtually) chair the workshop’s panel (with Helen Hulsker, Carlos Castillo (ChaTo), Liangjie Hong, and Robyn Rap). And hope the rest of the panel will take Helen’s suggestion to heart and book a ☕️ meeting with their lawyer colleagues soon to discuss matters of privacy, compliance, and ethics in the context of AI in HR 😉. 👍 I was impressed by the thorough infrastructures and shared/reusable job and job seeker representations that serve as a foundational component for many downstream products at LinkedIn (as told by Liangjie Hong during his keynote), 👏 Inspired by the strong ties between UX Research and Data Science at Indeed.com as shared by Robyn Rap in her keynote 💪 Proud for seeing our former interns Adam Mehdi Arafan and Roan Schellingerhout present their master theses at the workshop — work that came out of their internships with us at Randstad Groep Nederland!
Many thanks to my co-organizers, in particular the local Seattle team Chris Johnson and Toine Bogers, but also my remote fellows Mesut Kaya and Sepideh Mesbah for pulling an all-nighter 🌛.
Who knows, perhaps we meet again in Singapore next year 😁 (#RecSys2023).
📅 September 6, 2022 •
🕐 14:40 •
🏷 Blog and Research
🎉 A little success to share: three of our former data science interns at the Data Science chapter at Randstad Groep Nederland have written and published their master theses at our upcoming RecSys in HR Workshop; an academic workshop that revolves around AI in HR, which is part of an ACM International Conference on Recommender Systems (the AI systems used for matching; whether it is Netflix movies to users, or in our case; jobs to job seekers).
As always, the work of the students is pretty technical, but I will go ahead and try to provide little human-understandable summaries below.
Explainable Career Path Predictions using Neural Models
Roan Schellingerhout worked under supervision of Volodymyr Medentsiy on Explainable Career Path Prediction using Neural Networks, where he trained deep neural networks on our own talent work history data, to create a tool that can help consultants or talents to predict possible career switches, given as input a talent’s work history. The predictions are visually explained, in the sense that the underlying reasons for proposing a certain job are provided. Roan tested these visualizations on consultants, and found consultants generally like them.
End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates
A. M. Arafan, D. Graus, F. P. Santos, and E. Beauxis-Aussalet, “End-to-end bias mitigation in candidate recommender systems with fairness gates,” in Recsys in hr’22: the 2nd workshop on recommender systems for human resources, 2022. [Bibtex]
@inproceedings{arafan2022end,
author = {Arafan, Adam Mehdi and Graus, David and Santos, Fernando P. and Beauxis-Aussalet, Emma},
title = {End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates},
year = {2022},
booktitle = {RecSys in HR’22: The 2nd Workshop on Recommender Systems for Human Resources },
numpages = {8},
location = {Seattle, WA, USA and Online},
month={9}
}
Adam Arafan worked under supervision of myself on “End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates,” in his thesis he experimented with making the SmartMatch Talent Recommender more fair (at the level of gender), either by changing the “input” of the algorithm (for example; by balancing male and female candidates in the training data), or by changing its “output” (for example: for a given list of candidates, go through the list to make sure the top 10 has a 50/50 balance between male and female candidates). His work is novel because these type of “bias mitigation” strategies have been studied in isolation, but never together.
Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks
B. Platten, M. Macfarlane, D. Graus, and S. Mesbah, “Automated personnel scheduling with reinforcement learning and graph neural networks,” in Recsys in hr’22: the 2nd workshop on recommender systems for human resources, 2022. [Bibtex]
@inproceedings{platten2022automated,
author = {Platten, Benjamin and Macfarlane, Matthew and Graus, David and Mesbah, Sepideh},
title = {Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks},
year = {2022},
booktitle = {RecSys in HR’22: The 2nd Workshop on Recommender Systems for Human Resources },
numpages = {10},
location = {Seattle, WA, USA and Online},
month={9}
}
Ben Platten worked under supervision of Sepideh Mesbah on Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks, in which he experimented with “reinforcement learning” (a specific machine learning paradigm) which in theory suits the challenging task of scheduling well. He experimented on a toy problem to assess that, indeed, the method seems to work quite well.
Very proud of the latest cohort of Data Science thesis interns at Randstad Groep Nederland. In absence of a “real” defense at the University of Amsterdam, we organized our own afternoon packed with defenses (and subsequent drinks) in our Randstad HQ in Diemen. At the end of the afternoon we were able to congratulate Roan, Anna, and Adam on a job (almost) well done!
Roan Schellingerhout presented his work on “Explainable career Path Predictions.” Roan implemented explainable deep neural nets for predicting and explaining a job seekers’ next opportunity, given their previous. He evaluated the models intrinsically, in addition to testing them (+ their explanations) with actual recruiters, and found both that models are accurate and recruiters like and understand them.
Anna Lőrincz worked on data-to-text generation, and fine-tuned a multilingual transformer model for generating benefits (salary, contract, working hours, locations) in job descriptions in both Dutch and English, given structured information (numeric, categorical, and binary variables). She found that transformers can successfully generate fluent and correct text given structured inputs, confirmed that inputs or prompts have a high impact on performance, and found that her approach beats template-based methods in textual diversity. She also found a few very funny hallucinated work locations (“pal achter centraal station in Zwaaijdijk”, was one of our favorites), and found that transformer models tend to sometimes correct output (adjusting a 3k/hour salary into a 3k monthly salary).
Finally, Adam Mehdi Arafan presented his “Double Fair-Gated Bias Mitigation Pipeline” for our Talent Recommender system, where he studied bias in multiple parts of our recsys pipeline, from re-balancing training data (to simulate both balaned and highly imbalanced scenarios), to generating additional balanced synthetic data, and re-ranking outputs. Turns out applying synthetic data does not only help in creating more fair rankers, but can also have benefits in terms of model accuracy!
All three students did great jobs, stay tuned for their thesises (and, who knows, publications? 😏)
Fantastic news! We’ve received word the 2nd edition of our “Recommender Systems for Human Resources” (RecSys in HR) Workshop has been accepted to be included in the ACM RecSys 2022 program, to be held in Seattle!
Last year’s (first) edition of our workshop was co-located with ACM RecSys 2021 in Amsterdam, and featured two keynotes, a panel, breakout sessions and 8 paper presentations. The recording, workshop proceedings, and a workshop report are available through our workshop’s website at: https://recsyshr2021.aau.dk/
Check back there soon for information on the 2022 edition we’re planning with Toine Bogers, Mesut Kaya, Francisco Gutiérrez, and newly joined co-organizers Sepideh Mesbah (Randstad Groep Nederland) and Chris Johnson (Indeed.com)!
I’m giving a talk at the KINTalks series, organized by the KIN Center for Digital Innovation on March 25. It’s going to be a hybrid event, so happy to meet you at the VU Amsterdam, and if not, see you online! RSVP here on EventBrite.
KINTalks is a hybrid event where practitioners are invited to talk about their work experience regarding innovation and digital technology.
The blurb
At Randstad, the global leader in the HR services industry, searching and matching is at the heart of what we do. Being founded in 1960, We know from our heritage that real connections are not made from data and algorithms alone – they require human involvement. Last year, we helped more than two million job seekers find a meaningful job by combining industry-scale recommender and search systems with our distinct human touch. While many opportunities exist, employing AI in recruitment and HR is considered high-risk by the European Commission’s proposed regulatory framework on AI, which will bring additional requirements, obligations, and constraints.
In this hybrid talk, I will explain some of the characteristics of, challenges, and opportunities in the HR domain from an AI perspective. I will share some of our own work in recommendations, algorithmic matching, algorithmic bias and knowledge graphs, and highlight some of the ongoing research in this domain.
N. Vermeer, V. Provatorova, D. Graus, T. Rajapakse, and S. Mesbah, “Using robbert and extreme multi-label classification to extract implicit and explicit skills from dutch job descriptions,” in Compjobs ’22: the first international workshop on computational jobs marketplace, 2022. [Bibtex]
@inproceedings{vermeer2022using,
author = {Vermeer, Ninande and Provatorova, Vera and Graus, David and Rajapakse, Thilina and Mesbah, Sepideh},
title = {Using RobBERT and eXtreme Multi-Label Classification to Extract Implicit and Explicit Skills From Dutch Job Descriptions},
year = {2022},
booktitle = {CompJobs '22: The First International Workshop on Computational Jobs Marketplace},
numpages = {5},
location = {Online},
month={2}
}
☝️ Ninande Vermeer worked under supervision of Sepideh Mesbah and Vera Provatorova (UvA) on: “Using RobBERT and eXtreme Multi-Label Classification to Extract Implicit and Explicit Skills From Dutch Job Descriptions” in which we study to what extent a RobBERT-XMLC model can be used to extract explicit and implicit skills from Dutch job descriptions.
S. van Els, D. Graus, and E. Beauxis-Aussalet, “Improving fairness assessments with synthetic data: a practical use case with a recommender system for human resources,” in Compjobs ’22: the first international workshop on computational jobs marketplace, 2022. [Bibtex]
@inproceedings{vanels2022improving,
author = {van Els, Sarah-Jane and Graus, David and Beauxis-Aussalet, Emma},
title = {Improving Fairness Assessments with Synthetic Data: a Practical Use Case with a Recommender System for Human Resources},
year = {2022},
booktitle = {CompJobs '22: The First International Workshop on Computational Jobs Marketplace},
numpages = {5},
location = {Online},
month={2}
}
✌️ Sarah-Jane van Els worked under supervision of myself and Emma Beauxis-Aussalet (Civic AI Lab) on “Improving Fairness Assessments with Synthetic Data: a Practical Use Case with a Recommender System for Human Resources” in which we explore approaches and methods for assessing algorithmic bias by using synthetic data to improve the size and representativity of a test set used for training candidate recommender systems.
👏 Proud of our former interns for having published their work! And happy with the collaborations we have had with our co-authors 😁.
Update: unfortunately but understandably (in light of the pandemic), this conference has been post-poned until further notice.
December 9th I will join a panel discussion on AI and equal opportunities in the labor market (together with Siri Beerends & Rina Joosten-Rabou), at a conference organized by WOMEN INC. An offline event in Utrecht (Corona volente)!
For those who missed it — like myself 😏 — we have now published the full recording of our Recommender Systems in Human Resources (RecSys in HR) Workshop, which was held on October 1st, in conjunction with the ACM RecSys Conference in Amsterdam.
Our workshop included two keynotes, eight paper presentations, breakout sessions, a virtual panel on the topics of the upcoming EU framework on AI, Fair & inclusive HR Tech, and how to “activate hidden workers.” See the full program here.
So, if you have 4h33m to spare, see the full recording below!
(or use the convenient YouTube chapter to jump through the program 😅)
Many thanks to all co-organizers, contributors, paper authors, and participants, both virtual and in-person! And hopefully we’ll see each other at the second edition ✌️.
Another academic year, another (short)list of potential projects. Are you a final-year AI or data science student, interested in doing an internship with us? Reach out! First, read below why you would want to join us, and scroll further down for the following project descriptions:
Job Description Generation (NLP)
Conversational/QA approaches for resume information extraction (NLP)
Segmentation of resumes (NLP, CV)
Synthetic data for bias mitigation in recommender systems
![[PDF]](https://graus.nu/blog/wp-content/plugins/papercite/img/pdf.png)
