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/

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

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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

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With Giovanni Mauro from Scuola Normale Superiore and Istituto di scienza e tecnologie dell'informazione "A. Faedo" (Cnr-Isti) we discuss how AI influences users and how users influence AI back and the urba`n impact of this feedback loop. Don't miss this third episode of targz!

Link to the paper: https://link.springer.com/article/10.1007/s10994-025-06904-z

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In this second episode of targz we talk about intrinsic dimension in neural networks and why they it is important to understand what's happening inside them. Also, do you know what a manifold is?

Link to the paper: https://arxiv.org/abs/1905.12784

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Is it possible that adding random documents to an LLM prompt can actually improve the response? Well, let's find it out in this first episode of targz!

Link to the paper: https://arxiv.org/pdf/2401.14887

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