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#6: Purpose-Aware Privacy-Preserving Recommendations with Manel Slokom

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Manage episode 329684869 series 3288795
Content provided by Marcel Kurovski. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Marcel Kurovski or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In episode number six, we welcome Manel Slokom to the show and talk about purpose-aware privacy-preserving data for recommender systems. Manel is a 4th year PhD student at Delft University of Technology. For three years in a row she served as student volunteer at RecSys - before becoming student volunteer co-chair herself in 2021. Besides working on privacy and fairness, she also dedicates herself to simulation and in particular synthetic data for recommender systems - also co-organizing the 1st SimuRec Workshop as part of RecSys 2021.

This episode is definitely worth a longer run. Manel and I discussed fairness and privacy in recommender systems and how ratings can leak signals about sensitive personal information. For example, classifiers may exploit ratings in order to effectively determine one's gender. She explains "Personalized Blurring", which is the approach she developed to personalize gender obfuscation in user rating data, as well as how this can contribute to more diverse recommendations.
In our discussion, we also touch "data-centric AI", a term recently formulated by Andrew Ng, and how adapting feedback data may yield underestimated effects on recommendations that can lead to "data-centric recommender systems". In addition, we dived into the differences between simulated and synthetic data which brought us to the SimuRec workshop that she co-organized as part of RecSys 2021.

Finally, Manel provides some recommendations for young researcher to become active RecSys community members and benefit from exchange: talk to people and volunteer at RecSys.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Links from the Episode:

Papers:

General Links:

  continue reading

23 episodes

Artwork
iconShare
 
Manage episode 329684869 series 3288795
Content provided by Marcel Kurovski. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Marcel Kurovski or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In episode number six, we welcome Manel Slokom to the show and talk about purpose-aware privacy-preserving data for recommender systems. Manel is a 4th year PhD student at Delft University of Technology. For three years in a row she served as student volunteer at RecSys - before becoming student volunteer co-chair herself in 2021. Besides working on privacy and fairness, she also dedicates herself to simulation and in particular synthetic data for recommender systems - also co-organizing the 1st SimuRec Workshop as part of RecSys 2021.

This episode is definitely worth a longer run. Manel and I discussed fairness and privacy in recommender systems and how ratings can leak signals about sensitive personal information. For example, classifiers may exploit ratings in order to effectively determine one's gender. She explains "Personalized Blurring", which is the approach she developed to personalize gender obfuscation in user rating data, as well as how this can contribute to more diverse recommendations.
In our discussion, we also touch "data-centric AI", a term recently formulated by Andrew Ng, and how adapting feedback data may yield underestimated effects on recommendations that can lead to "data-centric recommender systems". In addition, we dived into the differences between simulated and synthetic data which brought us to the SimuRec workshop that she co-organized as part of RecSys 2021.

Finally, Manel provides some recommendations for young researcher to become active RecSys community members and benefit from exchange: talk to people and volunteer at RecSys.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Links from the Episode:

Papers:

General Links:

  continue reading

23 episodes

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