『targz』のカバーアート

targz

targz

著者: Luca Florio
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

In targz we brigde the gap between industry and academia in the IT world. In every episode I will interview a researcher that will explain a paper that is representative of their work. We will try to keep it short an simple, so that anyone working in IT can enjoy and understand the paper, no Ph.D. required. Fasten your seat belt, open your mind, and get ready to unpack a tarball of compressed Computer Science knowledge!Luca Florio
エピソード
  • EP06 - Does Fair Ranking Lead to Fair Recruitment? With Dr. Carlos Castillo
    2026/03/23

    Everyone would like a fair recruitment process, but unfortunately the reality is way more complex than just fixing some sorting algorithm. In this episode of targz Dr. Carlos Castillo, aka ChaTo, from ICREA describes the research conducted by his group to address the issue.

    Want to know more? checkout the paper: https://www.sciencedirect.com/science/article/pii/S0306457325004479
    Here you can also find more information about the project: http://findhr.eu/

    続きを読む 一部表示
    19 分
  • EP05 - Exposing Cross-Platform Coordinated Inauthentic Activity in theRun-Up to the 2024 U.S. Election. With Dr. Marco Minici
    2026/03/09

    In this episode of targz Marco Minici, Researcher at ICAR-CNR, describes how to identify group of users coordinating on different social platform that try to influence other people opinions.

    Link to the paper (Exposing Cross-Platform Coordinated Inauthentic Activity in the Run-Up to the 2024 U.S. Election): https://arxiv.org/pdf/2410.22716v3

    If you want to keep up with every new episode of targz, follow me on:

    • LikedIn: https://www.linkedin.com/in/elleflorio/
    • Bluesky: https://bsky.app/profile/florio.dev
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    17 分
  • EP04 - Post-Training Denoising of User Profiles withLLMs in Collaborative Filtering Recommendation. With Ervin Dervishaj
    2026/02/23

    When it comes to recommendation, indirect feedback by user is a powerful tool, but it can be problematic to deal with noise. In this episode of targz Ervin Dervishaj from University of Copenhagen presents a method to leverage LLMs for post-training denoising. How does it work? What are the benefits? Let's find out together!


    Link to the paper (Post-Training Denoising of User Profiles withLLMs in Collaborative Filtering Recommendation): https://arxiv.org/pdf/2601.18009


    If you want to keep up with every new episode of targz, follow me on:

    - LikedIn: https://www.linkedin.com/in/elleflorio/

    - Bluesky: https://bsky.app/profile/florio.dev

    続きを読む 一部表示
    14 分
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