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 to support their goals and needs as well as how the user-centric evaluation framework fits into all of this. In our interview, Martijn outlines...

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 specific for recommendations in the travel industry. In our interview, Bram elaborates on bandit feedback and how it is used in practice. We...

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 of music and podcast streaming personalization at Spotify. In our interview, Himan walks us through popularity bias as the main cause of...

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 and domains which need particular attention with respect to non-traditional populations. Sole outlines some of the major challenges like lacking...

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 how he transitioned from robotics into personalization as well as how the Microsoft recommenders repository started. We learn more about the three...

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 started with tackling unfairness. In our discussion, Michael outlines the difference and similarity between fairness and bias. We discuss several...

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 potency of simple algorithms and ways to mitigate popularity bias in data and recommendations. We also cover collaborative filtering and various...

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. Daniel presents two examples from YouTube’s ad performance forecasting to demonstrate the bandwidth of use cases for user modeling. We also discuss...

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 learning to perform personalized page construction. Deep content understanding drives the creation of useful groupings of videos to be shown in a...

In episode number 12 of Recsperts we meet Rishabh Mehrotra, the Director of Machine Learning at ShareChat and former Staff Research Scientist & Area Tech Lead at Spotify. We discuss user need, intent and satisfaction, contrast discovery with diversity and learn about marketplace and multi-stakeholder recommenders. Rishabh also introduces us into the creator economy at ShareChat.

In episode number 11 of Recsperts we meet Flavian Vasile who is a Principal Scientist at Criteo AI Lab. We dive into the specifics of personalized advertising and talk about click versus conversion optimization. Flavian also walks us through alternative recommender modelling approaches like economic and generative recommendations.

In episode ten of Recsperts we discuss the application of recommender systems to the human resources domain for matching people with jobs. I talk to David Graus, the Data Science Chapter Lead at Randstad which provides HR services to clients worldwide. David shares how recommender systems can support human recruiters by proposing the right candidates for vacancies. We also learn more about the biases that can play a role in that process and how to address them.

In episode nine of Recsperts we introduce RecPack which is the new recommender package for Python for easy, consistent and extensible experimentation and benchmarking. I talk to Lien Michiels and Robin Verachtert who are both industrial PhD students at the University of Antwerp. They also share how they provide modularized personalization for customers in the news and ecommerce sector at Froomle. In adition, we learn more about their research on filter bubbles as well as recommender model...

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 as the role of fairness in the music domain. Christine published work at many conferences like CHI, CHIIR, ICIS, and WWW.

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.

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...

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,...

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.

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.

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.

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

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.