Two papers accepted at the RecSys in HR Workshop!

πŸ“… August 23, 2021 β€’ πŸ• 12:10 β€’ 🏷 Research

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!

  • [PDF] M. de Groot, J. Schutte, and D. Graus, “Job posting-enriched knowledge graph for skills-based matching,” in Recsys in hr 2021, Amsterdam, Netherlands, 2021.
    [Bibtex]
    @inproceedings{degroot2021job,
    author = {de Groot, Maurits and Schutte, Jelle and Graus, David},
    title = {Job Posting-Enriched Knowledge Graph for Skills-based Matching},
    year = {2021},
    booktitle = {RecSys in HR 2021},
    numpages = {9},
    location = {Amsterdam, Netherlands},
    address = {Amsterdam, Netherlands},
    month={10}
    }
  • [PDF] D. Lavi, V. Medentsiy, and D. Graus, “Consultantbert: fine-tuned siamese sentence-bert for matching jobs and job seekers,” in Recsys in hr 2021, Amsterdam, Netherlands, 2021.
    [Bibtex]
    @inproceedings{lavi2021consultantbert,
    author = {Lavi, Dor and Medentsiy, Volodymyr and Graus, David},
    title = {conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers},
    year = {2021},
    booktitle = {RecSys in HR 2021},
    numpages = {8},
    location = {Amsterdam, Netherlands},
    address = {Amsterdam, Netherlands},
    month={10}
    }

Curious to know what they’re about? I tweet better than I blog πŸ‘‡

Stay tuned for pre-prints! See the other accepted papers here.

Disclaimer: yes, I co-organize the workshop, but I was not involved with reviewing/decisions, we have a great (and independent) Program Committee for that!

Co-organizing “RecSys in HR” workshop at RecSys 2021!

πŸ“… March 18, 2021 β€’ πŸ• 12:14 β€’ 🏷 Blog and Research

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 be held in Amsterdam, 27th September-1st October 2021.

We wrote this workshop proposal with Toine Bogers (Aalborg University), Mesut Kaya (Aalborg University), Katrien Verbert (KU Leuven) and Francisco GutiΓ©rrez (KU Leuven), at the initiative/idea of Toine, who virtually approached me in RecSys 2020’s gather.town :-D. Toine and Mesut work on a large research project with Denmark’s largest online recruitment portal, JobIndex.

For now, check out our stunning stub page at https://recsyshr2021.aau.dk/ and stay tuned for updates!

“Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation” accepted at UMAP2020!

πŸ“… April 21, 2020 β€’ πŸ• 17:33 β€’ 🏷 Papers and Research

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 it: here, or from arXiv.

  • [PDF] [DOI] F. Lu, A. Dumitrache, and D. Graus, “Beyond optimizing for clicks: incorporating editorial values in news recommendation,” in Proceedings of the 28th acm conference on user modeling, adaptation and personalization, New York, NY, USA, 2020, p. 145–153.
    [Bibtex]
    @inproceedings{lu2020beyond,
    author = {Lu, Feng and Dumitrache, Anca and Graus, David},
    title = {Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation},
    year = {2020},
    isbn = {9781450368612},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3340631.3394864},
    doi = {10.1145/3340631.3394864},
    booktitle = {Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
    pages = {145–153},
    numpages = {9},
    keywords = {usefulness, news recommendation, editorial values},
    location = {Genoa, Italy},
    series = {UMAP ’20}
    }

Update 08/05: Cool, @NickKivits mentioned our paper in his Villamedia column: Het idee van de filterbubbel kan in de prullenbak and newsletter (with over 11k subscribers!)

I am particularly happy with this work because:

1️⃣ In our paper we show how you can align algorithm design across stakeholders (in this case: data scientists and journalists), by effectively modeling an editorial value (“dynamicness”) in the news recommender of Het Financieele Dagblad without losing accuracy.

2️⃣ We present (more) empirical proof that #recsys (can) offer(s) users *more* diverse, serendipitous, and dynamic lists of articles, compared to editorially curated lists, and hence (can) help in *avoiding*, not creating filter bubbles!

3️⃣ It is the perfect wrap-up of our Google DNI-funded “SMART Journalism” project at FD Mediagroep (we wrote most of the paper in our spare time after the project ended).

See below the video of the talk at UMAP 2020 below:

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