Recsperts - Recommender Systems Experts

Hosted by Marcel Kurovski

Technology ScienceInterviews guests

Website RSS feed

Episodes

Latest episode

May 2026

Language

About the show

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.

Listen to episodes

33 recent

May 12, 2026Episode 331 hr 48 min

#32: RecSys in the Delivery Industry at Wolt with Sasha Fedintsev

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 Remarks Links from the Episode: Alexander "Sasha" Fedintsev on LinkedIn Alexander on X Wolt Alexander Fedintsev at Wolt Tech Talks: Restaurant discovery with Wolt: Deep Neural Networks to power recommendations H3 Geospatial Indexing System Recommenders Repository Tanja Reilly: The Staff Engineer's Path Will Larson: Staff Engineer: Leadership beyond the management track Coupon collector's problem Alexander Fedintsev (2026): Longevity Bottlenecks: Part I — Dementia Papers: Rendle et al. (2009): BPR: Bayesian personalized ranking from implicit feedback He et al. (2017): Neural Collaborative Filtering Dacrema et al. (2019): Are we really making much progress? A worrying analysis of recent neural recommendation approaches Rendle et al (2020): Neural Collaborative Filtering vs. Matrix Factorization Revisited Hu et al. (2008): Collaborative Filtering for Implicit Feedback Datasets Grbovic et al. (2015): E-commerce in Your Inbox: Product Recommendations at Scale Quadrana et al. (2018): Sequence-Aware Recommender Systems Su et al. (2024): Long-Term Value of Exploration: Measurements, Findings and Algorithms Tran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation Lichtenberg et al. (2024): Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

February 19, 2026Episode 321 hr 37 min

#31: Psychology-Aware Recommender Systems with Elisabeth Lex

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 Remarks Links from the Episode: Elisabeth Lex on LinkedIn Website of Elisabeth AI for Society Lab First International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2024 Second International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2025 HyPer Workshop: Hybrid AI for Human-Centric Personalization Tutorial on Psychology-Informed RecSys ACT-R: Adaptive Control of Thought-Rational POPROX: Platform for OPen Recommendation and Online eXperimentation Papers: Elaine Rich (1979): User Modeling via Stereotypes Lex et al. (2021): Psychology-informed Recommender Systems Reiter-Haas et al. (2021): Predicting Music Relistening Behavior Using the ACT-R Framework Moscati et al. (2023): Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation Tran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

January 28, 2026Episode 311 hr 32 min

#30: Serendipity for Recommender Systems with Annelien Smets

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 Remarks Links from the Episode: Annelien Smets on LinkedIn Website of Annelien LinkedIn Article by Annelien Smets (2025): Overcoming the Paradox of Artificial Serendipity The Serendipity Society Serendipity Engine Papers: Smets (2025): Intended, afforded, and experienced serendipity: overcoming the paradox of artificial serendipity Smets et al. (2022): Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental Design Binst et al. (2025): What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems Ziarani et al. (2021): Serendipity in Recommender Systems: A Systematic Literature Review Chen et al. (2021): Values of User Exploration in Recommender Systems Smets et al. (2025): Why Do Recommenders Recommend? Three Waves of Research Perspectives on Recommender Systems Smets (2023): Designing for Serendipity, a Means or an End? General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

August 27, 2025Episode 301 hr 37 min

#29: Transformers for Recommender Systems with Craig Macdonald and Sasha Petrov

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 Remarks Links from the Episode: Craig Macdonald on LinkedIn Sasha Petrov on LinkedIn Sasha's Website Tutorial: 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 Recommendation Sun et al. (2019): BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer Petrov et al. (2022): A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation Petrov et al. (2022): Effective and Efficient Training for Sequential Recommendation using Recency Sampling Petrov 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 Sampling Petrov et al. (2025): Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss Petrov et al. (2024): RecJPQ: Training Large-Catalogue Sequential Recommenders Petrov et al. (2024): Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items Rajput et al. (2023): Recommender Systems with Generative Retrieval Petrov et al. (2023): Generative Sequential Recommendation with GPTRec Petrov et al. (2024): Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website Disclaimer: 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.

April 15, 2025Episode 291 hr 35 min

#28: Multistakeholder Recommender Systems with Robin Burke

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 Remarks Links from the Episode: Robin Burke on LinkedIn Robin's Website That Recommender Systems Lab Reference to Broder's Keynote on Computational Advertising and Recommender Systems from RecSys 2008 Multistakeholder Recommender Systems (from Recommender Systems Handbook), chapter by Himan Abdollahpouri & Robin Burke POPROX: The Platform for OPen Recommendation and Online eXperimentation AltRecSys 2024 (Workshop at RecSys 2024) Papers: Burke et al. (1996): Knowledge-Based Navigation of Complex Information Spaces Burke (2002): Hybrid Recommender Systems: Survey and Experiments Resnick et al. (1997): Recommender Systems Goldberg et al. (1992): Using collaborative filtering to weave an information tapestry Linden et al. (2003): Amazon.com Recommendations - Item-to-Item Collaborative Filtering Aird et al. (2024): Social Choice for Heterogeneous Fairness in Recommendation Aird et al. (2024): Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice Burke et al. (2024): Post-Userist Recommender Systems : A Manifesto Baumer et al. (2017): Post-userism Burke et al. (2024): Conducting Recommender Systems User Studies Using POPROX General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

March 19, 2025Episode 281 hr 27 min

#27: Recommender Systems at the BBC with Alessandro Piscopo and Duncan Walker

In 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. With a reach of about 500M+ users who access services every week there is a huge potential. My guests discuss the challenges of aligning recommendations with public service values and the role of editors and constant exchange, alignment, and learning between the algorithmic and editorial lines of recommender systems. We also discuss the potential of cross-domain recommendations to leverage the content across different products as well as the organizational setup of teams working on recommender systems at the BBC. We learn about skews in the data due to the nature of an online service that also has a linear offering with TV and radio services. Towards the end, we also touch a bit on QUARE @ RecSys, which is the Workshop on Measuring the Quality of Explanations in 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 (03:10) - About Alessandro Piscopo and Duncan Walker (14:53) - RecSys Applications at the BBC (20:22) - Journey of Building Public Service Recommendations (28:02) - Role and Implementation of Public Service Values (36:52) - Algorithmic and Editorial Recommendation (01:01:54) - Further RecSys Challenges at the BBC (01:15:53) - Quare Workshop (01:23:27) - Closing Remarks Links from the Episode: Alessandro Piscopo on LinkedIn Duncan Walker on LinkedIn BBC QUARE @ RecSys 2023 (2nd Workshop on Measuring the Quality of Explanations in Recommender Systems) Papers: Clarke et al. (2023): Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges Boididou et al. (2021): Building Public Service Recommenders: Logbook of a Journey Piscopo et al. (2019): Data-Driven Recommendations in a Public Service Organisation General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

February 19, 2025Episode 271 hr 35 min

#26: Diversity in Recommender Systems with Sanne Vrijenhoek

In 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 presents a normative framework for these metrics, linking them to different democratic theory perspectives. Beyond evaluation, we discuss how to optimize diversity in recommender systems and reflect on missed opportunities—such as the RecSys Challenge 2024, which could have gone beyond accuracy-chasing. Sanne also shares her recommendations for improving the challenge by incorporating objectives such as diversity. During our conversation, Sanne shares insights on effectively communicating recommender systems research to non-technical audiences. To wrap up, we explore ideas for fostering a more diverse RecSys research community, integrating perspectives from multiple disciplines. 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 Sanne Vrijenhoek (14:49) - What Does Diversity in RecSys Mean? (26:32) - Assessing Diversity in News Recommendations (34:54) - Rank-Aware Divergence Metrics to Measure Normative Diversity (01:01:37) - RecSys Challenge 2024 - Recommendations for the Recommenders (01:11:23) - RecSys Workshops - NORMalize and AltRecSys (01:15:39) - On the Different Conceptualizations of Diversity in RecSys (01:28:38) - Closing Remarks Links from the Episode: Sanne Vrijenhoek on LinkedIn Informfully MIND: MIcrosoft News Dataset RecSys Challenge 2024 NORMalize 2023: The First Workshop on the Normative Design and Evaluation of Recommender Systems NORMalize 2024: The Second Workshop on the Normative Design and Evaluation of Recommender Systems AltRecSys 2024: The AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation Papers: Vrijenhoek et al. (2021): Recommenders with a Mission: Assessing Diversity in News Recommendations Vrijenhoek et al. (2022): RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations Heitz et al. (2024): Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys Challenge Vrijenhoek et al. (2024): Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems Helberger (2019): On the Democratic Role of News Recommenders Steck (2018): Calibrated Recommendations General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

October 12, 2024Episode 2639 min

#25: RecSys 2024 Special

In 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 Website Papers: RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

October 1, 2024Episode 251 hr 21 min

#24: Video Recommendations at Facebook with Amey Dharwadker

In 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 challenges they face, such as gathering reliable user feedback signals to avoid pitfalls like watchbait. With a vast and ever-growing corpus of billions of videos—millions of which are added each month—the cold start problem looms large. We explore how content understanding, user feedback aggregation, and exploration techniques help address this issue. Amey explains how engagement metrics like watch time, comments, and reactions are used to rank content, ensuring users receive meaningful and diverse video feeds. A key highlight of the conversation is the importance of real-time personalization in fast-paced environments, such as short-form video platforms, where user preferences change quickly. Amey also emphasizes the value of cross-domain data in enriching user profiles and improving recommendations. Towards the end, Amey shares his insights on leadership in machine learning teams, pointing out the characteristics of a great ML team. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts. Don't forget to follow the podcast and please leave a review (00:00) - Introduction (02:32) - About Amey Dharwadker (08:39) - Video Recommendation Use Cases on Facebook (16:18) - Recommendation Teams and Collaboration (25:04) - Challenges of Video Recommendations (31:07) - Video Content Understanding and Metadata (33:18) - Multi-Stage RecSys and Models (42:42) - Goals and Objectives (49:04) - User Behavior Signals (59:38) - Evaluation (01:06:33) - Cross-Domain User Representation (01:08:49) - Leadership and What Makes a Great Recommendation Team (01:13:01) - Closing Remarks Links from the Episode: Amey Dharwadker on LinkedIn Amey's Website RecSys Challenge 2021 VideoRecSys Workshop 2023 VideoRecSys + LargeRecSys 2024 Papers: Mahajan et al. (2023): CAViaR: Context Aware Video Recommendations Mahajan et al. (2023): PIE: Personalized Interest Exploration for Large-Scale Recommender Systems Raul et al. (2023): CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systems Zhai et al. (2024): Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations Saket et al. (2023): Formulating Video Watch Success Signals for Recommendations on Short Video Platforms Wang et al. (2022): Surrogate for Long-Term User Experience in Recommender Systems Su et al. (2024): Long-Term Value of Exploration: Measurements, Findings and Algorithms General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website

August 16, 2024Episode 241 hr 54 min

#23: Generative Models for Recommender Systems with Yashar Deldjoo

In 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 to the main topic for this episode: generative models for recommender systems. We learn about their aspects for improving beyond the (partially saturated) state of traditional recommender systems: improve effectiveness and efficiency for top-n recommendations, introduce interactivity beyond classical conversational recsys, provide personalized zero- or few-shot recommendations. We learn about the modeling paradigms and as well about the scenarios for generative models which mainly differ by input and modelling approach: ID-based, text-based, and multimodal generative models. This is how we navigate the large field of acronyms leading us from VAEs and GANs to LLMs. Towards the end of the episode, we also touch on the evaluation, opportunities, risks and harms of generative models for recommender systems. Yashar also provides us with an ample amount of references and upcoming events where people get the chance to know more about GenRecSys. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts. Don't forget to follow the podcast and please leave a review (00:00) - Introduction (03:58) - About Yashar Deldjoo (09:34) - Motivation for RecSys (13:05) - Intro to Generative Models for Recommender Systems (44:27) - Modeling Paradigms for Generative Models (51:33) - Scenario 1: Interaction-Driven Recommendation (57:59) - Scenario 2: Text-based Recommendation (01:10:39) - Scenario 3: Multimodal Recommendation (01:24:59) - Evaluation of Impact and Harm (01:38:07) - Further Research Challenges (01:45:03) - References and Research Advice (01:49:39) - Closing Remarks Links from the Episode: Yashar Deldjoo on LinkedIn Yashar's Website KDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and Opportunities RecSys 2024 Workshop: The 1st Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommender Systems (ROEGEN@RECSYS'24) Papers: Deldjoo et al. (2024): A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys) Deldjoo et al. (2020): Recommender Systems Leveraging Multimedia Content Deldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks Deldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models Liang et al. (2018): Variational Autoencoders for Collaborative Filtering He et al. (2016): Visual Bayesian Personalized Ranking from Implicit Feedback General Links: Follow me on LinkedIn Follow me on X Send me your comments, questions and suggestions to marcel.kurovski@gmail.com Recsperts Website