RecSys in HR Workshop Recording available

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 πŸ˜…)

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 ✌️.

Algorithmic bias and bias mitigation talk at the anti-discrimination hackathon

That was fun! The Online Anti-Discrimination Hackathon ran last weekend, a hakacthon co-organized by Ministerie van OCW, Inspectie SZW, Ministerie van BZK, and Hackathon Factory which revolved around “gender discrimination in data collection and labeling for automated assessment and selection of candidates.” Having ample experience in designing and developing AI-powered candidate selection systems, and the risks of algorithmic bias, I was happy to contribute to this hackathon with a few colleagues at Randstad Groep Nederland in several ways.

still of me giving the talk in front of a blue wall

Talk on Algorithmic Bias Mitigation in Automated Recruitment

First, I gave a (virtual, pre-recorded) talk on algorithmic matching, algorithmic bias, and bias mitigation in the domain of automated recruitment. More specifically, I shared how and where we use AI and recommender systems to facilitate job and candidate matching at Randstad Groep Nederland, and more generally about the challenges of bias and the opportunities of bias mitigation. I showcased both examples of misuse of AI, which results in discriminatory systems, and examples of how AI can be used to actively reduce or mitigate bias in the recruitment process. See the recording of my talk below:

In addition, me and a colleague joined live interactive roundtable sessions during the hackathon, and we brought a panel of four subject-matter experts for one-on-one sessions with hackathon teams and participants.

Read more

“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 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:

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

“RecSys in the Media Industry” Lecture at RecSys Summer School

With Daan Odijk I gave a lecture + hands-on workshop at the ACM Summer School on Recommender Systems in Gothenburg, Sweden on RecSys in the Media Industry: Relevance, Recency, Popularity, and Diversity.

πŸ“Έ by Alan Said

For it, we had a long (90+ min) lecture combining insights, experiences, and projects from our work at RTL and Blendle (Daan), and FD Mediagroep (me).

In addition, we did a small hands-on workshop, implementing a content-based re-ranker for WikiNews.

See our slides and notebooks here: https://github.com/graus/recsys_summer_school/

See a tweet by @alansaid, here:

Finally, see my slidedeck here:

Reading News with a Purpose: Explaining User Profiles for Self-Actualization

Really excited to have co-authored “Reading News with a Purpose,” which was accepted at the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data (ExHUM), at UMAP 2019!

With the largest list of authors (ranging from philosophers via polcomm researchers to computer scientists), from a wide array of institutions; Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Darcia Wilkinson, and Nava Tintarev!

This is work that came out of our ICT with Industry project “Opening the black box of user profiles in content-based recommender systems” where we (FD Mediagroep) collaborated with Nava Tintarev and our excellent team of academics in a week-long academic hackathon!

Read the pre-print, below:

  • [PDF] [DOI] E. Sullivan, D. Bountouridis, J. Harambam, S. Najafian, F. Loecherbach, M. Makhortykh, D. Kelen, D. Wilkinson, D. Graus, and N. Tintarev, “Reading news with a purpose: explaining user profiles for self-actualization,” in Adjunct publication of the 27th conference on user modeling, adaptation and personalization, 2019, p. 241–245.
    [Bibtex]
    @inproceedings{sullivan2019reading,
    title={Reading news with a purpose: Explaining user profiles for self-actualization},
    author={Sullivan, Emily and Bountouridis, Dimitrios and Harambam, Jaron and Najafian, Shabnam and Loecherbach, Felicia and Makhortykh, Mykola and Kelen, Domokos and Wilkinson, Daricia and Graus, David and Tintarev, Nava},
    booktitle={Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization},
    pages={241--245},
    year={2019},
    url={https://doi.org/10.1145/3314183.3323456},
    doi={10.1145/3314183.3323456}
    }

Read the original idea that sparked the project, presented at the 2nd FATREC Workshop at RecSys 2018, here:

  • [PDF] D. Graus, M. Sappelli, and D. M. Chu, “Let me tell you who you are,” in The 2nd fatrec workshop on responsible recommendation, 2018.
    [Bibtex]
    @inproceedings{graus2018let,
    title={Let me tell you who you are},
    author={Graus, David and Sappelli, Maya and Chu, Dung Manh},
    booktitle={The 2nd FATREC Workshop on Responsible Recommendation},
    year={2018}
    }

Position paper “β€œLet Me Tell You Who You are” β€” Explaining Recommender Systems by Opening Black Box User Profiles”

Our position paper “β€œLet Me Tell You Who You are” β€” Explaining Recommender Systems by Opening Black Box User Profiles” was accepted at the 2nd FATREC Workshop on Responsible Recommendation, held at RecSys ’18!

In this paper, we detail some our ideas and approaches of providing transparency in recommendations through displaying the user profiles, used ‘internally’ by our recommender system. Read the pre-print below!

  • [PDF] D. Graus, M. Sappelli, and D. M. Chu, “Let me tell you who you are,” in The 2nd fatrec workshop on responsible recommendation, 2018.
    [Bibtex]
    @inproceedings{graus2018let,
    title={Let me tell you who you are},
    author={Graus, David and Sappelli, Maya and Chu, Dung Manh},
    booktitle={The 2nd FATREC Workshop on Responsible Recommendation},
    year={2018}
    }
FATREC Position paper: Explaining recommender systems by opening black box user profiles