Displaying episodes 1 - 8 of 8 in total

#7: Behavioral Testing with RecList for Recommenders with Jacopo Tagliabue

Episode number seven of Recsperts deals with behavioral testing for recommender systems. I talk to Jacopo Tagliabue, who is the founder of tooso and now director of artificial intelligence at Coveo. He made many contributions to various conferences like SIGIR, WWW, or RecSys. One of them is RecList, which provides behavioral, black-box testing for recommender systems.

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

Episode number six of Recsperts is about purpose-aware privacy-preserving data for recommender systems. My guest is Manel Slokom, who is a 4th year PhD student at Delft University of Technology. She served as student volunteer at RecSys for three years in a row before becoming student volunteer co-chair herself in 2021. In addition to her work 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.

#5: Fashion Recommendations with Zeno Gantner

Episode number five of Recsperts revolves around fashion recommendations in general and at Zalando in specific. My guest is Zeno Gantner, who is a principal applied scientist and works in one of several personalization teams at Zalando. As an individual contributor and part of the leadership team he drives personalization not only to recommend relevant clothing, but also to facilitate inspiration and discovery for Zalando’s customers. With a background in computer science and symbolic AI, Zeno spent his PhD on ML applied to recommender systems and contributed to various open source projects as well as served at RecSys as member of the senior program committee.

#4: Adversarial Machine Learning for Recommenders with Felice Merra

In episode four my guest is Felice Merra, who is an applied scientist at Amazon. Felice obtained his PhD from Politecnico di Bari where he was a researcher at the Information Systems Lab (SisInf Lab). He investigated Security and Adversarial Machine Learning in Recommender Systems by looking at different ways to perturb interaction or content data, but also model parameters, and elaborated various defense strategies.

#3: Bandits and Simulators for Recommenders with Olivier Jeunen

In episode three I am joined by Olivier Jeunen, who is a postdoctoral scientist at Amazon. Olivier obtained his PhD from University of Antwerp with his work "Offline Approaches to Recommendation with Online Success". His work concentrates on Bandits, Reinforcement Learning and Causal Inference for Recommender Systems.

#2: Deep Learning based Recommender Systems with Even Oldridge

In episode two I am joined by Even Oldridge, Senior Manager at NVIDIA, who is leading the Merlin Team. These people are working on an open-source framework for building large-scale deep learning recommender systems and have already won numerous RecSys competitions.

#1: Practical Recommender Systems with Kim Falk

In this first interview we talk to Kim Falk, Senior Data Scientist, multiple RecSys Industry Chair and author of the book "Practical Recommender Systems"

#0: Launching Recsperts - the Recommender Systems Experts Podcast

In this first episode of Recsperts - Recommender Systems Experts I will introduce this new podcast show where we will have lots of interviews with experts in the field of recommender systems. From academia to industry, from application to theory - this podcast will cover all the topics in recommender systems.