Shows
Recsperts - Recommender Systems Experts#31: Psychology-Aware Recommender Systems with Elisabeth LexIn episode 31 of Recsperts, I sit down with Elisabeth Lex, Full Professor of Human-Computer Interfaces and Inclusive Technologies at Graz University of Technology and a leading researcher at the intersection of recommender systems, psychology, and human–computer interaction. Together, we explore how recommender systems can become truly human-centric by integrating cognitive, emotional, and personality-aware models into their design.Elisabeth begins by addressing a common reductionism in the field: treating users primarily as data points rather than as humans with goals, emotions, memories, and cognitive boundaries. We revisit the origins of psychology-informed recommendation, including the Gr...
2026-02-191h 37Recsperts - Recommender Systems Experts#30: Serendipity for Recommender Systems with Annelien SmetsIn episode 30 of Recsperts, I speak with Annelien Smets, Professor at Vrije Universiteit Brussel and Senior Researcher at imec-SMIT, about the value, perception, and practical design of serendipity in recommender systems. Annelien introduces her framework for understanding serendipity through intention, experience, and affordances, and explains the paradox of artificial serendipity - why it cannot be engineered, but only designed for.We start by unpacking the paradox of serendipity: while serendipity cannot be engineered or planned, systems and environments can be designed to increase the likelihood that serendipitous experiences occur. Annelien explains why randomness alone...
2026-01-281h 32Recsperts - Recommender Systems Experts#29: Transformers for Recommender Systems with Craig Macdonald and Sasha PetrovIn episode 29 of Recsperts, I welcome Craig Macdonald, Professor of Information Retrieval at the University of Glasgow, and Aleksandr “Sasha” Petrov, PhD researcher and former applied scientist at Amazon. Together, we dive deep into sequential recommender systems and the growing role of transformer models such as SASRec and BERT4Rec.Our conversation begins with their influential replicability study of BERT4Rec, which revealed inconsistencies in reported results and highlighted the importance of training objectives over architecture tweaks. From there, Craig and Sasha guide us through their award-winning research on making transformers for sequential recommendation with...
2025-08-271h 37Recsperts - Recommender Systems Experts#28: Multistakeholder Recommender Systems with Robin BurkeIn episode 28 of Recsperts, I sit down with Robin Burke, professor of information science at the University of Colorado Boulder and a leading expert with over 30 years of experience in recommender systems. Together, we explore multistakeholder recommender systems, fairness, transparency, and the role of recommender systems in the age of evolving generative AI.We begin by tracing the origins of recommender systems, traditionally built around user-centric models. However, Robin challenges this perspective, arguing that all recommender systems are inherently multistakeholder—serving not just consumers as the recipients of recommendations, but also content providers, platform op...
2025-04-151h 35Recsperts - Recommender Systems Experts#27: Recommender Systems at the BBC with Alessandro Piscopo and Duncan WalkerIn episode 27 of Recsperts, we meet Alessandro Piscopo, Lead Data Scientist in Personalization and Search, and Duncan Walker, Principal Data Scientist in the iPlayer Recommendations Team, both from the BBC. We discuss how the BBC personalizes recommendations across different offerings like news or video and audio content recommendations. We learn about the core values for the oldest public service media organization and the collaboration with editors in that process.The BBC once started with short video recommendations for BBC+ and nowadays has to consider recommendations across multiple domains: news, the iPlayer, BBC Sounds, BBC Bytesize, and more...
2025-03-191h 27Recsperts - Recommender Systems Experts#26: Diversity in Recommender Systems with Sanne VrijenhoekIn episode 26 of Recsperts, I speak with Sanne Vrijenhoek, a PhD candidate at the University of Amsterdam’s Institute for Information Law and the AI, Media & Democracy Lab. Sanne’s research explores diversity in recommender systems, particularly in the news domain, and its connection to democratic values and goals.We dive into four of her papers, which focus on how diversity is conceptualized in news recommender systems. Sanne introduces us to five rank-aware divergence metrics for measuring normative diversity and explains why diversity evaluation shouldn’t be approached blindly—first, we need to clarify the underlying values. She also...
2025-02-191h 35Recsperts - Recommender Systems Experts#25: RecSys 2024 SpecialIn episode 25, we talk about the upcoming ACM Conference on Recommender Systems 2024 (RecSys) and welcome a former guest to geek about the conference. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction
(01:56) - Overview RecSys 2024
(07:01) - Contribution Stats
(09:37) - Interview
Links from the Episode:RecSys 2024 Conference WebsitePapers:RecSys '24: Proceedings of the 18th ACM Conference on Recommender SystemsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions...
2024-10-1239 minRecsperts - Recommender Systems Experts#24: Video Recommendations at Facebook with Amey DharwadkerIn episode 24 of Recsperts, I sit down with Amey Dharwadker, Machine Learning Engineering Manager at Facebook, to dive into the complexities of large-scale video recommendations. Amey, who leads the Video Recommendations Quality Ranking team at Facebook, sheds light on the intricate challenges of delivering personalized video feeds at scale. Our conversation covers content understanding, user interaction data, real-time signals, exploration, and evaluation techniques.We kick off the episode by reflecting on the inaugural VideoRecSys workshop at RecSys 2023, setting the stage for a deeper discussion on Facebook’s approach to video recommendations. Amey walks us through the critical ch...
2024-10-011h 21Recsperts - Recommender Systems Experts#23: Generative Models for Recommender Systems with Yashar DeldjooIn episode 23 of Recsperts, we welcome Yashar Deldjoo, Assistant Professor at the Polytechnic University of Bari, Italy. Yashar's research on recommender systems includes multimodal approaches, multimedia recommender systems as well as trustworthiness and adversarial robustness, where he has published a lot of work. We discuss the evolution of generative models for recommender systems, modeling paradigms, scenarios as well as their evaluation, risks and harms.We begin our interview with a reflection of Yashar's areas of recommender systems research so far. Starting with multimedia recsys, particularly video recommendations, Yashar covers his work around adversarial robustness and trustworthiness leading...
2024-08-161h 54Recsperts - Recommender Systems Experts#22: Pinterest Homefeed and Ads Ranking with Prabhat Agarwal and Aayush MudgalIn 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 and content modeling, short- vs. long-term objectives, evaluation as well as multi-task learning and touch on counterfactual evaluation as well.In our interview, Prabhat guides us through the journey of continuous improvements of Pinterest's Homefeed personalization starting with techniques such as gradient boosting over two-tower models to DCN...
2024-06-061h 24Recsperts - Recommender Systems Experts#21: User-Centric Evaluation and Interactive Recommender Systems with Martijn WillemsenIn 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 the reasons for providing users control over recommendations and how to holistically evaluate the satisfaction and usefulness of recommendations for users goals and needs. We discuss the...
2024-04-081h 35Recsperts - Recommender Systems Experts#20: Practical Bandits and Travel Recommendations with Bram van den AkkerIn 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 discuss off-policy- and on-policy-bandits, and we learn that counterfactual evaluation is right for selecting the best model...
2023-11-161h 45Recsperts - Recommender Systems Experts#19: Popularity Bias in Recommender Systems with Himan AbdollahpouriIn 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 unfair recommendations for multiple stakeholders. We discuss the consumer- and provider-side implications and how to evaluate popularity bias. Not the sheer existence of popularity bias...
2023-10-121h 41Recsperts - Recommender Systems Experts#18: Recommender Systems for Children and non-traditional PopulationsIn 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 public datasets or multifaceted criteria for the suitability of recommendations. The highly dynamic needs and abilities of children pose proper user modeling as a crucial...
2023-08-171h 39Recsperts - Recommender Systems Experts#17: Microsoft Recommenders and LLM-based RecSys with Miguel FierroIn 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 key components: examples, library, and tests. With more than 900 tests and more than 30 different algorithms, this...
2023-06-151h 02Recsperts - Recommender Systems Experts#16: Fairness in Recommender Systems with Michael D. EkstrandIn 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 stages at which biases can enter the system as well as how bias can indeed support mitigating unfairness. We also cover the perspectives of different stakeholders with...
2023-05-171h 42
Recsperts - Recommender Systems Experts#15: Podcast Recommendations in the ARD Audiothek with Mirza KlimentaIn 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 approaches for content-based podcast recommendations, drawing on Mirza's expertise in multidimensional scaling for graph drawings. Additionally, Mirza sheds light on the responsibility...
2023-04-271h 19
Recsperts - Recommender Systems Experts#14: User Modeling and Superlinked with Daniel SvonavaIn 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 sources of information that fuel user models and additional personlization tasks that benefit from it like user onboarding. We learn that th...
2023-03-151h 43
Recsperts - Recommender Systems Experts#13: The Netflix Recommender System and Beyond with Justin BasilicoThis 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 personalized homepage.Justin and I discuss the misalignment of metrics as just one out of many elements that is making personalization still “super ha...
2023-02-151h 20
Recsperts - Recommender Systems Experts#12: From User Intent to Multi-Stakeholder Recommenders and Creator Economy with Rishabh MehrotraIn 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 have to perceive real-time user interaction data conditioned on user intents. We learn that relevance does not imply satisfaction as well as that diversity and discovery are two very different concepts.Rishabh takes us even further on his industry research journey where we also touch...
2023-01-182h 05
Recsperts - Recommender Systems Experts#11: Personalized Advertising, Economic and Generative Recommenders with Flavian VasileIn 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 optimization and transition to econ(omic) reco(mmendations), a new approach to model the effect of a recommendations system on the users' decision making process. Economic theory plays an important role for this conceptual shift towards better recommender systems.In addition...
2022-12-151h 11
Recsperts - Recommender Systems Experts#10: Recommender Systems in Human Resources with David GrausIn 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 recruiters at Randstad. We also learn which biases might have an influence when using recommenders for decision support in the recruiting process as well as how Randstad mitigates them.In this episode we learn more about another domain where recommender systems can serve humans...
2022-11-161h 03
Recsperts - Recommender Systems Experts#9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin VerachtertIn 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 and who work for Froomle. We also hear about their research on filter bubbles as well as model drift along with their RecSys 2022 contributions.In this episode we introduce RecPack as a new recommender package that is easy to use and...
2022-09-151h 27
Recsperts - Recommender Systems Experts#8: Music Recommender Systems, Fairness and Evaluation with Christine BauerIn 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.In this episode we talk about the specifics of recommenders in the music streaming domain. In particular, we discuss the interests of different stakeholders, like...
2022-08-151h 10
Digital Future – Technologie & UnternehmenskulturRecommender Systems und PersonalisierungAnwendungsbereiche und Hintergründe mit Marcel Kurovski
Anwendungsbereiche und Hintergründe mit Marcel Kurovski
Recommender-Experte Marcel Kurovski beschreibt verschiedene Typen von Recommender Systemen und ihre Anwendungsgebiete.
Paper aus der Folge:
Tapestry - ein 1992 als wahrscheinlich erstes Recommender System auf Basis von Collaborative Filtering: “Using Collaborative Filtering to Weave an Information Tapestry” (Goldberg et al., 1992) https://dl.acm.org/doi/pdf/10.1145/138859.138867
Cross-Domain Recommender Systems: “Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste” (Nazari et al., Spotify, 2020): https://arxiv.org/pdf/2007.13287.pdf
Item-basierte Empfehlungen bei Amazon: “Amazon.com Recommendations: Item-to-Item Collaborative Filtering” (Linden et al., Amazon...
2022-07-151h 47Recsperts - Recommender Systems Experts#7: Behavioral Testing with RecList for Recommenders with Jacopo TagliabueIn 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 conferences like SIGIR, WWW, or RecSys. In addition, he serves as adjunct professor at NYU.In this episode we introduce behavioral testing for recommender systems and the corresponding framework RecList that was created by Jacopo and his co-authors. Behavioral testing goes beyond pure retrieval accuracy metrics...
2022-07-071h 01Recsperts - Recommender Systems Experts#6: Purpose-Aware Privacy-Preserving Recommendations with Manel SlokomIn 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...
2022-05-251h 39Recsperts - Recommender Systems Experts#5: Fashion Recommendations with Zeno GantnerIn 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 recommendations impacting over 48 million customers in Europe.We discuss the ramifications and limitations of positive-only implicit feedback, touch on how reinforcement learning and more rating-like feedback can help as well as how to treat multiple feedback levels. In the main part, we turn...
2022-05-031h 24Recsperts - Recommender Systems Experts#4: Adversarial Machine Learning for Recommenders with Felice MerraIn 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 data, but also model parameters, and elaborated various defense strategies.In addition, we touch on the motivation of individuals or whole platforms to perform attacks and look at some examples that Felice has been working on throughout his research.
2022-02-231h 09Recsperts - Recommender Systems Experts#3: Bandits and Simulators for Recommenders with Olivier JeunenIn 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 evaluating online performance of recommender systems in an offline fashion and based on rich logging data. These methods stem from fields like bandit theory and reinforcement learning. They heavily rely on simulators whose benefits, requirements and limitations we discuss in greater detail. We further discuss...
2022-01-031h 12
knowITFolge 7 - Recommender SystemsIn dieser Episode beschäftigen wir uns mit dem spannenden Thema der "Recommender Systems". Dazu haben wir mit Marcel Kurovski einen Experten eingeladen, der sich seit Jahren mit dem Thema beschäftigt und uns tiefe Einblicke in die Anwendung und die Forschung hierzu gibt.
Show notes:
Kim Falk: Practical Recommender Systems: https://www.manning.com/books/practical-recommender-systems (In Episode 1 meines Podcasts spreche ich auch mit Kim über sein Buch und dort ist auch noch ein 37% Rabattcode in den Shownotes)
Recommender Systems Handbook: https://link.springer.com/book/10.1007/978-1-4899-7637-6 (wurde natürlich auch schon geleakt...
2021-11-1838 minRecsperts - Recommender Systems Experts#2: Deep Learning based Recommender Systems with Even OldridgeIn 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 systems as well as the challenges and pitfalls of deep learning based recommender systems. We briefly touch on Even's early data science contributions at PlentyOfFish, a Canadian online-dating platform. Starting with personalized recommendations of people to people he transitioned to realtor, a real-estate...
2021-10-3150 min
Recsperts - Recommender Systems Experts#1: Practical Recommender Systems with Kim FalkIn 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 mathematical deep-dive yet, but expect it to follow. In addition, we reason what constitutes good recommendations and briefly touch on a couple of ways of finding that out.Looking a bit into the history of the recommender systems community, we touch on the Netflix Prize that was running from 2006...
2021-10-081h 19
Recsperts - Recommender Systems Experts#0: Launching Recsperts - the Recommender Systems Experts PodcastHave 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: recommender systems.Whether you are a beginner and new to the field or you have already build recommenders, this show is to bring you the experts in recommender systems to share their knowledge and expertise with all of us. It is...
2021-09-2313 min
TechtiefenRecommender Systems
Recommendations, also Empfehlungen, sind mindestens so alt wie das Orakel von Delphi und der Hauptbestand zahlreicher Dienstleistungsberufe. Recomendation Systems hingegen sind ein spezieller Bereich des Information Retrieval und erst durch Amazon, Netflix und Spotify wirklich populär geworden. In dieser ausführlichen Techtiefenfolge erklärt Marcel Kurovski mit zahlreichen Beispielen das wesentliche Vorgehen dieser “Informationsaggregationsmaschinen”, welche von Collaborative Filtering über Matrixfaktorisierung bis zu Deep Learning reichen. Wir sprechen über die unterschiedlichen Stufen von Personalisierung und worin der Unterschied zur Suche besteht. Die Vor- und Nachteile von Relevanz als wichtigste Metrik für Recommender Systems kommen zur Sprache, genauso wie alternativ...
2020-07-132h 09