Couverture de Recsperts - Recommender Systems Experts

Recsperts - Recommender Systems Experts

Recsperts - Recommender Systems Experts

De : Marcel Kurovski
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 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 Mathématiques Science
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    • #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 h et 32 min
    • #29: Transformers for Recommender Systems with Craig Macdonald and Sasha Petrov

      #29: Transformers for Recommender Systems with Craig Macdonald and Sasha Petrov
      Aug 27 2025
      In 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 large corpora both more effective and more efficient. We discuss how recency sampling (RSS) reduces training times dramatically, and how gSASRec overcomes the problem of overconfidence in models trained with negative sampling. By generalizing the sigmoid function (gBCE), they were able to reconcile cross-entropy–based optimization results with negative sampling, matching the effectiveness of softmax approaches while keeping training scalable for large corpora.We also explore RecJPQ, their recent work on joint product quantization for item embeddings. This approach makes transformer-based sequential recommenders substantially faster at inference and far more memory-efficient for embeddings—while sometimes even improving effectiveness thanks to regularization effects. Towards the end, Craig and Sasha share their perspective on generative approaches like GPTRec, the promises and limits of large language models in recommendation, and what challenges remain for the future of sequential recommender systems.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don’t forget to follow the podcast and please leave a review.(00:00) - Introduction(04:09) - About Craig Macdonald(04:46) - About Sasha Petrov(13:48) - Tutorial on Transformers for Sequential Recommendations(19:24) - SASRec vs. BERT4Rec(21:25) - Replicability Study of BERT4Rec for Sequential Recommendation(32:52) - Training Sequential RecSys using Recency Sampling(40:01) - gSASRec for Reducing Overconfidence by Negative Sampling(01:00:51) - RecJPQ: Training Large-Catalogue Sequential Recommenders(01:21:37) - Generative Sequential Recommendation with GPTRec(01:29:12) - Further Challenges and Closing RemarksLinks from the Episode:Craig Macdonald on LinkedInSasha Petrov on LinkedInSasha's WebsiteTutorial: Transformers for Sequential Recommendation (ECIR 2024)Tutorial Recording from ACM European Summer School in Bari (2024)Talk: Neural Recommender Systems (European Summer School in Information Retrieval 2024)Papers:Kang et al. (2018): Self-Attentive Sequential RecommendationSun et al. (2019): BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from TransformerPetrov et al. (2022): A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationPetrov et al. (2022): Effective and Efficient Training for Sequential Recommendation using Recency SamplingPetrov et al. (2024): RSS: Effective and Efficient Training for Sequential Recommendation Using Recency Sampling (extended version)Petrov et al. (2023): gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative SamplingPetrov et al. (2025): Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE lossPetrov et al. (2024): RecJPQ: Training Large-Catalogue Sequential RecommendersPetrov et al. (2024): Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of ItemsRajput et al. (2023): Recommender Systems with Generative RetrievalPetrov et al. (2023): Generative Sequential Recommendation with GPTRecPetrov et al. (2024): Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts WebsiteDisclaimer:Craig holds concurrent appointments as a Professor of Information Retrieval at University of Glasgow and as an Amazon Scholar. This podcast describes work performed at the University of Glasgow and is not associated with Amazon.
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      1 h et 37 min
    • #28: Multistakeholder Recommender Systems with Robin Burke

      #28: Multistakeholder Recommender Systems with Robin Burke
      Apr 15 2025
      In 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 operators, and other key players with partially competing interests. He explains why the common “Recommended for You” label is, at best, an oversimplification and how greater transparency is needed to show how stakeholder interests are balanced.Our conversation also delves into practical approaches for handling multiple objectives, including reranking strategies versus integrated optimization. While embedding multistakeholder concerns directly into models may be ideal, reranking offers a more flexible and efficient alternative, reducing the need for frequent retraining.Towards the end of our discussion, we explore post-userism and the impact of generative AI on recommendation systems. With AI-generated content on the rise, Robin raises a critical concern: if recommendation systems remain overly user-centric, generative content could marginalize human creators, diminishing their revenue streams. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction(03:24) - About Robin Burke and First Recommender Systems(26:07) - From Fairness and Advertising to Multistakeholder RecSys(34:10) - Multistakeholder RecSys Terminology(40:16) - Multistakeholder vs. Multiobjective(42:43) - Reciprocal and Value-Aware RecSys(59:14) - Objective Integration vs. Reranking(01:06:31) - Social Choice for Recommendations under Fairness (01:17:40) - Post-Userist Recommender Systems(01:26:34) - Further Challenges and Closing RemarksLinks from the Episode:Robin Burke on LinkedInRobin's WebsiteThat Recommender Systems LabReference to Broder's Keynote on Computational Advertising and Recommender Systems from RecSys 2008Multistakeholder Recommender Systems (from Recommender Systems Handbook), chapter by Himan Abdollahpouri & Robin BurkePOPROX: The Platform for OPen Recommendation and Online eXperimentationAltRecSys 2024 (Workshop at RecSys 2024)Papers:Burke et al. (1996): Knowledge-Based Navigation of Complex Information SpacesBurke (2002): Hybrid Recommender Systems: Survey and ExperimentsResnick et al. (1997): Recommender SystemsGoldberg et al. (1992): Using collaborative filtering to weave an information tapestryLinden et al. (2003): Amazon.com Recommendations - Item-to-Item Collaborative FilteringAird et al. (2024): Social Choice for Heterogeneous Fairness in RecommendationAird et al. (2024): Dynamic Fairness-aware Recommendation Through Multi-agent Social ChoiceBurke et al. (2024): Post-Userist Recommender Systems : A ManifestoBaumer et al. (2017): Post-userismBurke et al. (2024): Conducting Recommender Systems User Studies Using POPROXGeneral 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 h et 35 min
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