Best Audiothek Podcasts (2024)

1
#22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal 1:24:07

1M ago1:24:07

1:24:07

In episode 22 of Recsperts, we welcome Prabhat Agarwal, Senior ML Engineer, and Aayush Mudgal, Staff ML Engineer, both from Pinterest, to the show. Prabhat works on recommendations and search systems at Pinterest, leading representation learning efforts. Aayush is responsible for ads ranking and privacy-aware conversion modeling. We discuss user an…

1
#21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen 1:35:46

3M ago1:35:46

1:35:46

In episode 21 of Recsperts, we welcome Martijn Willemsen, Associate Professor at the Jheronimus Academy of Data Science and Eindhoven University of Technology. Martijn's researches on interactive recommender systems which includes aspects of decision psychology and user-centric evaluation. We discuss how users gain control over recommendations, how…

1
#20: Practical Bandits and Travel Recommendations with Bram van den Akker 1:45:06

8M ago1:45:06

1:45:06

In episode 20 of Recsperts, we welcome Bram van den Akker, Senior Machine Learning Scientist at Booking.com. Bram's work focuses on bandit algorithms and counterfactual learning. He was one of the creators of the Practical Bandits tutorial at the World Wide Web conference. We talk about the role of bandit feedback in decision making systems and in …

1
#19: Popularity Bias in Recommender Systems with Himan Abdollahpouri 1:41:37

9M ago1:41:37

1:41:37

In episode 19 of Recsperts, we welcome Himan Abdollahpouri who is an Applied Research Scientist for Personalization & Machine Learning at Spotify. We discuss the role of popularity bias in recommender systems which was the dissertation topic of Himan. We talk about multi-objective and multi-stakeholder recommender systems as well as the challenges …

1
#18: Recommender Systems for Children and non-traditional Populations 1:39:33

11M ago1:39:33

1:39:33

In episode 18 of Recsperts, we hear from Professor Sole Pera from Delft University of Technology. We discuss the use of recommender systems for non-traditional populations, with children in particular. Sole shares the specifics, surprises, and subtleties of her research on recommendations for children. In our interview, Sole and I discuss use cases…

1
#17: Microsoft Recommenders and LLM-based RecSys with Miguel Fierro 1:02:59

1y ago1:02:59

1:02:59

In episode 17 of Recsperts, we meet Miguel Fierro who is a Principal Data Science Manager at Microsoft and holds a PhD in robotics. We talk about the Microsoft recommenders repository with over 15k stars on GitHub and discuss the impact of LLMs on RecSys. Miguel also shares his view of the T-shaped data scientist. In our interview, Miguel shares ho…

1
#16: Fairness in Recommender Systems with Michael D. Ekstrand 1:42:43

1y ago1:42:43

1:42:43

In episode 16 of Recsperts, we hear from Michael D. Ekstrand, Associate Professor at Boise State University, about fairness in recommender systems. We discuss why fairness matters and provide an overview of the multidimensional fairness-aware RecSys landscape. Furthermore, we talk about tradeoffs, methods and receive practical advice on how to get …

1
#15: Podcast Recommendations in the ARD Audiothek with Mirza Klimenta 1:19:11

1y ago1:19:11

1:19:11

In episode 15 of Recsperts, we delve into podcast recommendations with senior data scientist, Mirza Klimenta. Mirza discusses his work on the ARD Audiothek, a public broadcaster of audio-on-demand content, where he is part of pub. Public Value Technologies, a subsidiary of the two regional public broadcasters BR and SWR. We explore the use and pote…

1
#14: User Modeling and Superlinked with Daniel Svonava 1:43:13

1+ y ago1:43:13

1:43:13

In episode number 14 of Recsperts we talk to Daniel Svonava, CEO and Co-Founder of Superlinked, delivering user modeling infrastructure. In his former role he was a senior software engineer and tech lead at YouTube working on ad performance prediction and pricing. We discuss the crucial role of user modeling for recommendations and discovery. Danie…

1
#13: The Netflix Recommender System and Beyond with Justin Basilico 1:20:32

1+ y ago1:20:32

1:20:32

This episode of Recsperts features Justin Basilico who is director of research and engineering at Netflix. Justin leads the team that is in charge of creating a personalized homepage. We learn more about the evolution of the Netflix recommender system from rating prediction to using deep learning, contextual multi-armed bandits and reinforcement le…

1
#12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh Mehrotra 2:05:03

1+ y ago2:05:03

2:05:03

In this episode of Recsperts we talk to Rishabh Mehrotra, the Director of Machine Learning at ShareChat, about users and creators in multi-stakeholder recommender systems. We learn more about users intents and needs, which brings us to the important matter of user satisfaction (and dissatisfaction). To draw conclusions about user satisfaction we ha…

1
#11: Personalized Advertising, Economic and Generative Recommenders with Flavian Vasile 1:11:28

1+ y ago1:11:28

1:11:28

In this episode of Recsperts we talk to Flavian Vasile about the work of his team at Criteo AI Lab on personalized advertising. We learn about the different stakeholders like advertisers, publishers, and users and the role of recommender systems in this marketplace environment. We learn more about the pros and cons of click versus conversion optimi…

1
#10: Recommender Systems in Human Resources with David Graus 1:03:23

1+ y ago1:03:23

1:03:23

In episode number ten of Recsperts I welcome David Graus who is the Data Science Chapter Lead at Randstad Groep Nederland, a global leader in providing Human Resource services. We talk about the role of recommender systems in the HR domain which includes vacancy recommendations for candidates, but also generating talent recommendations for recruite…

1
#9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin Verachtert 1:27:20

2y ago1:27:20

1:27:20

In episode number nine of Recsperts we talk with the creators of RecPack which is a new Python package for recommender systems. We discuss how Froomle provides modularized personalization for customers in the news and e-commerce sectors. I talk to Lien Michiels and Robin Verachtert who are both industrial PhD students at the University of Antwerp a…

1
#8: Music Recommender Systems, Fairness and Evaluation with Christine Bauer 1:10:56

2y ago1:10:56

1:10:56

In episode number eight of Recsperts we discuss music recommender systems, the meaning of artist fairness and perspectives on recommender evaluation. I talk to Christine Bauer, who is an assistant professor at the University of Utrecht and co-organizer of the PERSPECTIVES workshop. Her research deals with context-aware recommender systems as well a…

1
#7: Behavioral Testing with RecList for Recommenders with Jacopo Tagliabue 1:01:56

2y ago1:01:56

1:01:56

In episode number seven, we meet Jacopo Tagliabue and discuss behavioral testing for recommender systems and experiences from ecommerce. Before Jacopo became the director of artificial intelligence at Coveo, he had founded tooso, which was later acquired by Coveo. Jacopo holds a PhD in cognitive intelligence and made many contributions to conferenc…

1
#6: Purpose-Aware Privacy-Preserving Recommendations with Manel Slokom 1:39:07

2y ago1:39:07

1:39:07

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 o…

1
#5: Fashion Recommendations with Zeno Gantner 1:24:06

2y ago1:24:06

1:24:06

In episode five my guest is Zeno Gantner, who is a principal applied scientist at Zalando. Zeno obtained his PhD from the University of Hildesheim where he was investigating ML-based recommender systems. As a principal applied scientist he is responsible for strategy, mentoring and setting standards for different initiatives on fashion recommendati…

1
#4: Adversarial Machine Learning for Recommenders with Felice Merra 1:09:17

2+ y ago1:09:17

1:09:17

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). There, he worked on Security and Adversarial Machine Learning in Recommender Systems. We talk about different ways to perturb interaction or content …

1
#3: Bandits and Simulators for Recommenders with Olivier Jeunen 1:12:54

2+ y ago1:12:54

1:12:54

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. We talk about methods for eval…

1
#2: Deep Learning based Recommender Systems with Even Oldridge 50:07

2+ y ago50:07

50:07

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. We talk about the relevance and impact of deep learning applied to recommender syste…

1
#1: Practical Recommender Systems with Kim Falk 1:19:39

3y ago1:19:39

1:19:39

In this first interview we talk to Kim Falk, Senior Data Scientist, multiple RecSys Industry Chair and author of the book "Practical Recommender Systems". We introduce into recommenders from a practical perspective discussing the fundamental difference between content-based and collaborative filtering as well as the cold-start problem - no mathemat…

1
#0: Launching Recsperts - the Recommender Systems Experts Podcast 13:29

3y ago13:29

13:29

Have you ever though about how Spotify is able to generate its fantastic Discover Weekly Playlist, how Amazon is generating a fortune by showing what other like you purchased in the past, or how Netflix achieves high user retention? The answer is personalization and in this show we focus on the most prominent way to achieve personalization: recomme…

Podcasts Worth a Listen

Audiothek Podcasts

Podcasts Worth a Listen

1
Recsperts - Recommender Systems Experts

Marcel Kurovski

1
#22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush Mudgal 1:24:07

1
#21: User-Centric Evaluation and Interactive Recommender Systems with Martijn Willemsen 1:35:46

1
#20: Practical Bandits and Travel Recommendations with Bram van den Akker 1:45:06

1
#19: Popularity Bias in Recommender Systems with Himan Abdollahpouri 1:41:37

1
#18: Recommender Systems for Children and non-traditional Populations 1:39:33

1
#17: Microsoft Recommenders and LLM-based RecSys with Miguel Fierro 1:02:59

1
#16: Fairness in Recommender Systems with Michael D. Ekstrand 1:42:43

1
#15: Podcast Recommendations in the ARD Audiothek with Mirza Klimenta 1:19:11

1
#14: User Modeling and Superlinked with Daniel Svonava 1:43:13

1
#13: The Netflix Recommender System and Beyond with Justin Basilico 1:20:32

1
#12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh Mehrotra 2:05:03

1
#11: Personalized Advertising, Economic and Generative Recommenders with Flavian Vasile 1:11:28

1
#10: Recommender Systems in Human Resources with David Graus 1:03:23

1
#9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin Verachtert 1:27:20

1
#8: Music Recommender Systems, Fairness and Evaluation with Christine Bauer 1:10:56

1
#7: Behavioral Testing with RecList for Recommenders with Jacopo Tagliabue 1:01:56

1
#6: Purpose-Aware Privacy-Preserving Recommendations with Manel Slokom 1:39:07

1
#5: Fashion Recommendations with Zeno Gantner 1:24:06

1
#4: Adversarial Machine Learning for Recommenders with Felice Merra 1:09:17

1
#3: Bandits and Simulators for Recommenders with Olivier Jeunen 1:12:54

1
#2: Deep Learning based Recommender Systems with Even Oldridge 50:07

1
#1: Practical Recommender Systems with Kim Falk 1:19:39

1
#0: Launching Recsperts - the Recommender Systems Experts Podcast 13:29

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