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Recsperts - Recommender Systems Experts

Recsperts - Recommender Systems Experts

By: Marcel Kurovski
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Summary

 Recommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. This podcast hosts the experts in recommender systems research and application. From understanding what users really want to driving large-scale content discovery - from delivering personalized online experiences to catering to multi-stakeholder goals. Guests from industry and academia share how they tackle these and many more challenges. With Recsperts coming from universities all around the globe or from various industries like streaming, ecommerce, news, or social media, this podcast provides depth and insights. We go far beyond your 101 on RecSys and the shallowness of another matrix factorization based rating prediction blogpost! The motto is: be relevant or become irrelevant! Expect a brand-new interview each month and follow Recsperts on your favorite podcast player.© 2026 Marcel Kurovski Mathematics Science
Episodes
  • #32: RecSys in the Delivery Industry at Wolt with Sasha Fedintsev

    #32: RecSys in the Delivery Industry at Wolt with Sasha Fedintsev
    May 12 2026
    In episode 32 of Recsperts, I’m joined by my colleague Sasha Fedintsev, Staff Applied Scientist at Wolt (DoorDash), working across personalization and ads, to unpack the realities of building large-scale recommender systems in food, grocery, and retail delivery. Together, we discuss the specifics of personalization in the delivery domain, and the models and ideas that power Wolt’s recommender system across 30+ markets - where theory quickly meets messy, high-stakes practice.We explore what makes this domain fundamentally different from traditional e-commerce: strong locality constraints, real-time context, and a heavy skew toward repurchasing behavior. Sasha explains how these factors break many textbook approaches - like standard collaborative filtering - and require creative adaptations such as clustering strategies and multi-stage ranking systems optimized for latency, all while respecting locality constraints.We also discuss the evolution of recommendation approaches over time - from classical collaborative filtering with ALS, to Neural Collaborative Filtering with BPR, and ultimately to transformer-based models for user sequence modeling and next-purchase prediction powering today’s venue ranking systems.We also touch on practical challenges such as evaluation in real-world systems, including A/B testing pitfalls and biases in logged data, as well as the complexity introduced by multi-surface experiences like discovery pages, vertical lists, and search. Beyond venues, we discuss why item-level recommendation is an order of magnitude harder - due to scale, context dependence, and availability constraints - and what this implies for future system design.Throughout the episode, Sasha provides a candid view on the evolving role of a Staff Applied Scientist - bridging research and production, setting scientific standards, and driving cross-team impact.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don’t forget to follow the podcast and please leave a review.(00:00) - Introduction(05:10) - About Sasha Fedintsev(15:26) - The Role of a Staff Applied Scientist(25:50) - Challenges and Specifics of the Delivery Industry(47:24) - Ranking and Recommendation Problems at Wolt(51:31) - NCF with BPR for Wolt's First DNN Recommendation Model(01:16:43) - User Sequence Transformers for Next Purchase Prediction(01:26:51) - Explore vs. Exploit or New vs. Recurring Purchases(01:31:29) - Ads Personalization at Wolt(01:36:16) - Further Challenges in RecSys(01:37:58) - A Final Note on Radical Longevity(01:46:30) - Closing RemarksLinks from the Episode:Alexander "Sasha" Fedintsev on LinkedInAlexander on XWoltAlexander Fedintsev at Wolt Tech Talks: Restaurant discovery with Wolt: Deep Neural Networks to power recommendationsH3 Geospatial Indexing SystemRecommenders RepositoryTanja Reilly: The Staff Engineer's PathWill Larson: Staff Engineer: Leadership beyond the management trackCoupon collector's problemAlexander Fedintsev (2026): Longevity Bottlenecks: Part I — DementiaPapers:Rendle et al. (2009): BPR: Bayesian personalized ranking from implicit feedbackHe et al. (2017): Neural Collaborative FilteringDacrema et al. (2019): Are we really making much progress? A worrying analysis of recent neural recommendation approachesRendle et al (2020): Neural Collaborative Filtering vs. Matrix Factorization RevisitedHu et al. (2008): Collaborative Filtering for Implicit Feedback DatasetsGrbovic et al. (2015): E-commerce in Your Inbox: Product Recommendations at ScaleQuadrana et al. (2018): Sequence-Aware Recommender SystemsSu et al. (2024): Long-Term Value of Exploration: Measurements, Findings and AlgorithmsTran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationLichtenberg et al. (2024): Ranking Across Different Content Types: The Robust Beauty of Multinomial BlendingGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website
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    1 hr and 49 mins
  • #31: Psychology-Aware Recommender Systems with Elisabeth Lex

    #31: Psychology-Aware Recommender Systems with Elisabeth Lex
    Feb 19 2026
    In 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 Grundy system -the first recommender system, built nearly 50 years ago - which framed book recommendation through stereotype modeling. From there, we discuss how the community’s focus shifted toward solving recommendation mainly as an algorithmic optimization problem, often sidelining richer models of human decision-making.We then map out the three major branches of psychology-informed RecSys - cognition-inspired, affect-aware, and personality-aware - and dive into practical examples. Elisabeth walks us through her work on modeling music re-listening behavior using cognitive architectures such as ACT-R (Adaptive Control of Thought–Rational) and shows how cognitive constructs like memory decay, attention, and familiarity can meaningfully augment standard approaches like collaborative filtering. We also explore how hybrid systems that combine cognitive models with collaborative filtering can yield not just higher accuracy but also more novelty, diversity, and clearer explanations.Our conversation also turns to user-centric evaluation. Elisabeth argues that accuracy metrics alone cannot tell us whether a system is genuinely helpful. Instead, we must measure attitudes, perceptions, motivations, and emotional responses - while carefully accounting for cognitive biases, UI effects, and users’ lived experiences.Towards the end, Elisabeth discusses emerging research directions such as hybrid AI (symbolic + sub-symbolic methods), the role of LLMs and agents, the risks of replacing human studies with automated evaluations, and the responsibility our community has to understand users beyond their clicks.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don’t forget to follow the podcast and please leave a review.(00:00) - Introduction(03:15) - About Elisabeth Lex(07:55) - Grundy, the first Recommender System (09:03) - Bridging the Gap between Psychology and Modern RecSys(17:21) - On how and when Elisabeth became a Researcher(21:39) - Survey on Psychology-Informed RecSys(39:29) - Personality-Aware Recommendation(49:43) - Affect- and Emotion-Aware Recommendation(01:01:37) - Cognition-Inspired Recommendation and the ACT-R Framework(01:14:39) - Combining Collaborative Filtering and ACT-R for Explainability(01:21:26) - Human-Centered Design(01:26:15) - Further Challenges and Closing RemarksLinks from the Episode:Elisabeth Lex on LinkedInWebsite of ElisabethAI for Society LabFirst International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2024Second International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2025HyPer Workshop: Hybrid AI for Human-Centric PersonalizationTutorial on Psychology-Informed RecSysACT-R: Adaptive Control of Thought-RationalPOPROX: Platform for OPen Recommendation and Online eXperimentationPapers:Elaine Rich (1979): User Modeling via StereotypesLex et al. (2021): Psychology-informed Recommender SystemsReiter-Haas et al. (2021): Predicting Music Relistening Behavior Using the ACT-R FrameworkMoscati et al. (2023): Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music RecommendationTran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website
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    1 hr and 37 mins
  • #30: Serendipity for Recommender Systems with Annelien Smets

    #30: Serendipity for Recommender Systems with Annelien Smets
    Jan 28 2026
    In 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 is not enough and why serendipity always emerges from an interplay between an unexpected encounter and a user’s ability to recognize its relevance and value.A central part of our discussion focuses on Annelien’s recent framework that distinguishes between intended, experienced, and afforded serendipity. We explore why organizations first need to clarify why they want serendipity - whether as an ideal, a common good, a mediator to achieve other goals (such as long-term retention or long-tail exposure), or even as a product feature in itself. From there, we dive into how users actually experience serendipity, drawing on qualitative interview research that identifies three core components: encounters must feel fortuitous, refreshing, and enriching. These components can manifest in different “flavors,” such as taste broadening, taste deepening, or rediscovering forgotten interests.We then move beyond algorithms to discuss affordances for serendipity - design principles that span content, user interfaces, and information access. Using examples from libraries, urban spaces, and digital platforms, Annelien shows why serendipity is a system-level property rather than a single metric or model tweak. We also discuss where serendipity can go wrong, including the Netflix “Surprise Me” feature, and why mismatched expectations can actually harm user experience.To close, we reflect on open research questions, from measuring different types of serendipity to understanding how content types, business models, and platform economics shape what is possible. Annelien also challenges a common myth: serendipity does not automatically burst filter bubbles—and should not be treated as a silver bullet.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don’t forget to follow the podcast and please leave a review.(00:00) - Introduction(03:57) - About Annelien Smets(14:42) - Paradox and Definition of (Artificial) Serendipity(27:04) - Intended Serendipity(43:01) - Experienced Serendipity(01:01:18) - Afforded Serendipity(01:13:49) - Examples of Serendipity Going Wrong(01:17:40) - Framework for Serendipity(01:22:41) - Further Challenges and Closing RemarksLinks from the Episode:Annelien Smets on LinkedInWebsite of AnnelienLinkedIn Article by Annelien Smets (2025): Overcoming the Paradox of Artificial SerendipityThe Serendipity SocietySerendipity EnginePapers:Smets (2025): Intended, afforded, and experienced serendipity: overcoming the paradox of artificial serendipitySmets et al. (2022): Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental DesignBinst et al. (2025): What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender SystemsZiarani et al. (2021): Serendipity in Recommender Systems: A Systematic Literature ReviewChen et al. (2021): Values of User Exploration in Recommender SystemsSmets et al. (2025): Why Do Recommenders Recommend? Three Waves of Research Perspectives on Recommender SystemsSmets (2023): Designing for Serendipity, a Means or an End?General Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website
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    1 hr and 32 mins
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