Category: Research
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FINDHR CV Data Donation Campaign
🗣️ Please consider donating your (anonymized) CV to advance research into bias mitigation in algorithmic hiring! With Randstad we are part of a consortium of research institutions (e.g., University of Amsterdam, Radboud Universiteit, Universitat Pompeu Fabra), civil society organizations (e.g., AlgorithmWatch), and companies (e.g., Adevinta) under the EU-funded FINDHR research project. The FINDHR project aims to: 1️⃣ create new ways to…
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“Transfer learning for multilingual vacancy text generation” preprint available
Anna Lőrincz‘ UvA MSc. data science thesis “Transfer learning for multilingual vacancy text generation” — which was graded a 9/10 💫 — was recently accepted at the The Second Version of Generation, Evaluation & Metrics (GEM) Workshop 2022 which will be held as part of EMNLP, December 7-11, 2022! Get the pre-print here: In her work, Anna explores…
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RecSys in HR 2022 Workshop Recording available
We have published the full recording of our RecSys in HR 2022 workshop, which we held September 22 in Seattle, WA, USA. The video is 5h42m43s long, so to guide you, I provide you the following list of highlights (see the video description for timestamps that will allow you to instantly skip to the sections described below): 1️⃣ Our first…
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Three papers accepted at RecSys in HR 2022 Workshop
🎉 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…
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RecSys in HR at ACM RecSys 2022 in Seattle!
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,…
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Two papers accepted at the RecSys in HR Workshop!
Happy to have learned we have two papers accepted at the first Recommender Systems in Human Resources Workshop, co-located with ACM RecSys 2021! These papers are the first academic publications of the Data Science Chapter at Randstad Groep Nederland! Curious to know what they’re about? I tweet better than I blog 👇 Stay tuned for…
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Co-organizing “RecSys in HR” workshop at RecSys 2021!
We received news that our workshop proposal “RecSys in HR: Workshop on Recommender Systems for Human Resources” was accepted for inclusion in the 15th ACM Conference on Recommender Systems (RecSys 2021) program! That means we’ll be running a full-day workshop with (research and position) papers, keynotes, and a panel (all TBD) during the conference which will…
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“Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation” accepted at UMAP2020!
The paper we wrote with former FD team mates Feng Lu and Anca Dumitrache has been accepted for publication as a long paper at UMAP 2020, the 28th Conference on User Modeling, Adaptation and Personalization! (I fondly remember my last time at UMAP, in 2016 😏) We have published a preprint of this paper, get…
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PodRecs: Workshop on Podcast Recommendations PC
I was invited to join the program committee of (the first) PodRecs: Workshop on Podcast Recommendations (to be held at RecSys’20). Since our work on BNR SMART Radio, I am really interested in the space of audio, recommender systems, and information retrieval. Curious to see the submissions! See the PodRecs call for papers, and check…
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“Improving automated segmentation of radio shows with audio embeddings”
Update (28/1/2020): Oberon’s thesis was accepted and will be published at the IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), to be held May 4-8 in Barcelona, Spain! The submission is co-authored with Klaus Lux and myself. Oberon Berlage recently successfully defended his MSc. thesis (title above!) for the Data Science…
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“The birth of collective memories” published in JASIST!
The journal paper “The birth of collective memories: Analyzing emerging entities in text streams” I wrote with Daan Odijk and Maarten de Rijke is now (finally) published at JASIST! It is published under OpenAccess/CC BY 4.0 and available in “early view” (published before it’s published) in the Wiley Online Library. Click on the image below…
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My PhD Thesis “Entities of Interest — Discovery in Digital Traces” is online!
My PhD thesis, Entities of Interest — Discovery in Digital Traces is now available for download. Click on the cover below to head to graus.nu/entities-of-interest and grab your electronic copy of the little booklet that took me 4+ years to write!
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James Chen Best Student Paper Award at UMAP 2016
Our paper, D. Graus, P. N. Bennett, R. W. White, and E. Horvitz, “Analyzing and predicting task reminders,” in Proceedings of the 2016 conference on user modeling adaptation and personalization, New York, NY, USA, 2016, p. 7–15. [Bibtex] @inproceedings{graus2016analyzing, author = {Graus, David and Bennett, Paul N. and White, Ryen W. and Horvitz, Eric}, title…
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Improving User Productivity with Automated Personal Assistants: Analyzing and Predicting Task Reminders
Update (16/07): This paper was awarded the James Chen Best Student Paper Award at UMAP! Automated personal assistants such as Google Now, Microsoft Cortana, Siri, M and Echo aid users in productivity-related tasks, e.g., planning, scheduling and reminding tasks or activities. In this paper we study one such feature of Microsoft Cortana: user-created reminders. Reminders…
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Dynamic Collective Entity Representations for Entity Ranking
Read a pre-print of our paper below: D. Graus, M. Tsagkias, W. Weerkamp, E. Meij, and M. de Rijke, “Dynamic collective entity representations for entity ranking,” in Proceedings of the ninth acm international conference on web search and data mining, New York, NY, USA, 2016, p. 595–604. [Bibtex] @inproceedings{graus2016dynamic, author = {Graus, David and Tsagkias, Manos…