“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 β€’ πŸ‘ 325

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:

(more…)

Improving automated segmentation of radio shows with audio embeddings published @ IEEE ICASSP2020

πŸ“… February 12, 2020 β€’ πŸ• 11:54 β€’ 🏷 Papers β€’ πŸ‘ 73

Oberon Berlage’s MSc. thesis: “Improving automated segmentation of radio shows with audio embeddings” which he wrote under my supervision during his internship at FD Mediagroep was awarded a 9/10, under condition that the work was publishable.

Turns out it was, as it was recently accepted at IEEE ICASSP2020 (the 45th International Conference on Acoustics, Speech, and Signal Processing) without any additional work/experiments (just a bit of reduction). But you already knew this… Oberon will be presenting this work in Barcelona, thanks to the generous support of UvA’s Information Studies program.

We now published a preprint, read it below:

  • [PDF] [DOI] O. Berlage, K. Lux, and D. Graus, “Improving automated segmentation of radio shows with audio embeddings,” in Icassp 2020 – 2020 ieee international conference on acoustics, speech and signal processing (icassp), 2020, pp. 751-755.
    [Bibtex]
    @inproceedings{berlage2020improving,
    author={O. {Berlage} and K. {Lux} and D. {Graus}},
    booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    title={Improving Automated Segmentation of Radio Shows with Audio Embeddings},
    year={2020},
    pages={751-755},
    doi={10.1109/ICASSP40776.2020.9054315},
    url={https://doi.org/10.1109/ICASSP40776.2020.9054315}
    }

His work revolved around improving BNR SMART Radio‘s text-based segmentation by incorporating audio signals in the form of audio embeddings. This turns out to improve over our text-based baseline by a whopping +32.3% F1-measure!

Even better: an audio-only approach, trained on a smallish openly available dataset, outperforms our text-only baseline by 9.4%. This means the segmentation method can be employed without need for audio transcription, which could be a money-saver.

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

πŸ“… April 10, 2019 β€’ πŸ• 11:15 β€’ 🏷 Papers β€’ πŸ‘ 183

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”

πŸ“… November 11, 2018 β€’ πŸ• 11:21 β€’ 🏷 Papers β€’ πŸ‘ 44

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

Pre-print of position paper “SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News”

πŸ“… June 1, 2018 β€’ πŸ• 13:44 β€’ 🏷 Papers β€’ πŸ‘ 152

Our position paperΒ “SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News” was accepted atΒ Algorithmic Personalization and News (APEN18) workshop, held at ICWSM ’18!

In this paper, we detail some of the ideas and opportunities of personalization in the domain of financial economic news. Read the pre-print below!

  • [PDF] M. Sappelli, D. M. Chu, B. Cambel, D. Graus, and P. Bressers, “Smart journalism: personalizing, summarizing, and recommending financial economic news,” in The algorithmic personalization and news (apen18) workshop at icwsm ’18, 2018.
    [Bibtex]
    @inproceedings{sappelli2018smart,
    title={SMART Journalism: Personalizing, Summarizing, and Recommending Financial Economic News},
    author={Sappelli, Maya and Chu, Dung Manh and Cambel, Bahadir and Graus, David and Bressers, Philippe},
    booktitle={The Algorithmic Personalization and News (APEN18) Workshop at ICWSM '18},
    year={2018}
    }

“The birth of collective memories” published in JASIST!

πŸ“… February 5, 2018 β€’ πŸ• 08:06 β€’ 🏷 Papers and Research β€’ πŸ‘ 70

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 to access it:

The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams

πŸ“… December 11, 2017 β€’ πŸ• 16:15 β€’ 🏷 Papers β€’ πŸ‘ 184

Our paper “The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams” was accepted for publication at JASIST (the Journal of the Association for Information Science and Technology)! Grab a pre-print here:

  • [PDF] [DOI] D. Graus, D. Odijk, and M. de Rijke, “The birth of collective memories: analyzing emerging entities in text streams,” Journal of the association for information science and technology, vol. 69, iss. 6, pp. 773-786, 2018.
    [Bibtex]
    @article{graus2018birth,
    author = {Graus, David and Odijk, Daan and de Rijke, Maarten},
    title = {The birth of collective memories: Analyzing emerging entities in text streams},
    journal = {Journal of the Association for Information Science and Technology},
    year = {2018},
    volume = {69},
    number = {6},
    pages = {773-786},
    doi = {10.1002/asi.24004},
    url = {https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/asi.24004},
    eprint = {https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/asi.24004},
    }

This paper is is:
1. My first journal paper
2. Based on Chapter 3 of my PhD thesis “Entities of Interest — Discovery in Digital Traces
3. The first collabo on a paperΒ (on paper) between the FD Mediagroep, Blendle, and the UvA
4. The tombstone on my academic career! (?)

In this paper we study news and social media streams spanning over 18 months, and comprising over 579 million documents, and analyze ’emergence patterns’ of entities, i.e., how a real-world entity (such as a person, organization, product, etc.) appears in these documents in the timespan between the entity’s first mention in online text streams, and when an article devoted to the entity is subsequently added to Wikipedia.