Recsperts - Recommender Systems Experts

In episode 34 of Recsperts, I'm joined by Raghav Saboo, Staff Machine Learning Engineer at DoorDash and Tech Lead for Personalization and Search for New Verticals — groceries, convenience, retail, alcohol, pet supplies and more, beyond the original restaurant vertical. We discuss the particular challenges of personalized recommendation, ranking and search in quick commerce, recent trends in generative recommendations and the application of Semantic IDs to item ranking and query reformulation. Raghav's path into recommender systems started in chemical engineering before he moved into ML consulting, a Master's in Statistics, Machine Learning and Econometrics from Duke University, and building LLMs for new language launches on Amazon's Alexa AI, ahead of joining DoorDash.

We start with the marketplace itself: DoorDash connects consumers, merchants and couriers, and growing it means balancing the interests of all three so that the platform stays healthy for everyone on it. Raghav walks me through how his team frames the consumer side around three pillars — familiarity (surfacing what a consumer already trusts), affordability (matching price sensitivity and timely deals) and novelty (introducing new items and categories without adding friction). From there we get into how DoorDash uses LLMs to build "memory blocks," structured natural-language representations of a consumer organized around semantic domains like dietary preference, pet ownership or trusted brands, and how these feed LLM-generated collections that get resolved into real items through embedding-based retrieval.

We then turn to DoorDash's move to generative approaches, centered on Semantic IDs: hierarchical product identifiers learned through recursive clustering of item content embeddings, forming a taxonomy that captures attributes a human-built catalog structure might miss — as Raghav puts it, "within e-commerce, items really carry a lot of meaning." He walks me through two production use cases: replacing dozens of taxonomy-based dense features in the ranking model with Semantic ID n-gram aggregations while improving online metrics, and using Semantic IDs for query reformulation in search, letting the system traverse a learned hierarchy to refine or diversify a query. This connects to DoorDash's own paper on the topic and to a broader conversation about why search, recommendation and agentic ordering — DoorDash's own "Ask DoorDash" — are converging on a shared substrate of Semantic IDs and consumer memory, while today's app surfaces still need to grow more flexible for that convergence to feel seamless.

We close with a preview of the RecSys 2026 tutorial "Recommender Systems in Delivery Platforms: Challenges, Solutions and Learnings," which Raghav is co-presenting with Wolt's Paavo Camps and myself, and his advice for navigating a field that reinvents itself every quarter: be honest about whether that pace suits you, use AI agents to filter what's worth your attention, and build the judgment to recognize dead ends early.

Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
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• (00:00) - Introduction

Links from the Episode:

• Raghav Saboo on LinkedIn
• Raghav Saboo's Substack
• Using LLMs to Infer Grocery Preferences from Restaurant Orders
• Building Ask DoorDash (Part 2): Intelligence
• Bridging Affordability, Familiarity, and Novelty (KDD 2025)
• Building a Unified Consumer Memory for Personalization at Scale
• Offline LLMs, Online Personalization: Generating Carousels at DoorDash
• RecSys 2026 Tutorials
• Workshop on Unified Search and Recommendation (USRW) 2026

Papers:

• Xu et al. (2026): One Hierarchy, Two Systems - Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation (RecSys 2026, USRW Workshop)
• Xi et al. (2026): Mine and Refine - Optimizing Graded Relevance in E-commerce Semantic Search Retrieval (CIKM 2026, Applied Research Track)
• Chen et al. (2026): Joint Optimization of Relevance and Engagement in Multi-Task Ranking for E-Commerce with Efficient LLM Supervision (SIGIR Industry Track)
• Sinha et al. (2025): Mind the Gap - Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations (RecSys 2025 GenAI Workshop Talk)
• Das et al. (2024): Applications of LLMs in E-Commerce Search and Product Knowledge Graph - The DoorDash Case Study (WSDM 2024)

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In episode 33 of Recsperts, I speak with Joseph A. Konstan, Distinguished McKnight University Professor and Distinguished University Teaching Professor at the University of Minnesota, co-founder of GroupLens, and the very first General Chair of the ACM RecSys conference back in 2007. This year he returns in that role as General Co-Chair of the 20th RecSys in Minneapolis. We talk about what actually makes a recommendation useful, why the field is more than a machine learning application, and how the community around RecSys came into being.

We start with what Joe cares about most: usefulness. He recalls the supermarket thought experiment of printing "buy bananas and bread" on every shopping cart and explains why the early systems were valuable because they were wrong a lot. A recommender that took ten people like you and often got it wrong was also often surprising when it got it right, whereas today's systems are wrong far less and useless far more. As Joe puts it, "I don't care about prediction at all. I care about changing people's behavior." We discuss why optimizing for click-through in news reliably produces clickbait, why leave-one-out evaluation only makes sense if you assume the user already knew the right answer and had simply forgotten it, and why usefulness can never be read off a single metric but depends on the task, the context and the breadth of the user's intent. Sometimes the most useful thing is not the recommendation itself, but the stars, the reviews or the comparison table you put around it.

From there we turn to the community itself. Joe traces it back to the spring of 1996 and the Berkeley Collaborative Filtering Workshop organized by Hal Varian and Paul Resnick, through a decade of scattered workshops at SIGIR, CSCW and CHI, to the first RecSys in 2007: a room on the Minnesota campus, over 120 people, and a substantial delegation from industry including Amazon. We discuss why the conference has always been heavily international and always at the intersection of research and practice, what surprised him most in 20 years (that we are still here and thriving), and the vision he pushed against the pull of becoming just another application of machine learning: a highly constrained, highly contextualized, multi-measure and often multi-stakeholder problem spanning algorithms, interfaces, data and business. This year the 20th RecSys comes home to Minneapolis, with Joe as General Co-Chair alongside George Karypis and Gediminas Adomavicius.

We close on where things are heading. Joe's advice for newcomers is to immerse yourself in an application and find the real problems and opportunities there, rather than arriving enamored with a tool and treating every nail as something to hit. He picks up Karl Higley's point from the POPROX team that the research community keeps focusing on the model when the real object is the system: your algorithm is useless if it is not embedded in something that can deliver its results in a useful way. And he names the work he is most excited about — human decision-making and consumer psychology in the context of choice, the business school perspective that brings marketing and pricing into the picture, multi-sided marketplaces with their often invisible market maker, and the ethics of these systems as a practical lens on the long-term value our metrics still fail to capture.

Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
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• (00:00) - Introduction

• Joseph Konstan on LinkedIn
• Website of Joseph Konstan
• GroupLens Research
• POPROX: Platform for OPen Recommendation and Online eXperimentation
• Net Perceptions
• LensKit: Python Recommendation Toolkit
• Recommender Systems Specialization on Coursera (Konstan & Ekstrand)
• ACM TechTalk by Joseph Konstan: Recommender Systems - Beyond Machine Learning
• ACM TechTalk by Joseph Konstan: Recommender Systems - The Power of Personalization
• RecSys: The ACM Conference on Recommender Systems
• TLDR AI Newsletter
• Associated Press

Papers:

• Resnick et al. (1994): GroupLens - An Open Architecture for Collaborative Filtering of Netnews
• Hill et al. (1995): Recommending and Evaluating Choices in a Virtual Community of Use
• Shardanand & Maes (1995): Social Information Filtering - Algorithms for Automating "Word of Mouth"
• Resnick & Varian (1997): Recommender Systems (CACM Special Issue)
• Sarwar et al. (2001): Item-Based Collaborative Filtering Recommendation Algorithms
• Das et al. (2007): Google News Personalization - Scalable Online Collaborative Filtering
• Su et al. (2023): Long-Term Value of Exploration - Measurements, Findings and Algorithms
• Tran et al. (2024): Transformers Meet ACT-R - Repeat-Aware and Sequential Listening Session Recommendation (Deezer)
• Ekstrand et al. (2024): Conducting Recommender Systems User Studies Using POPROX
• Higley et al. (2025): What News Recommendation Research Did (But Mostly Didn't) Teach Us About Building A News Recommender

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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.
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• (00:00) - Introduction

• 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

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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.

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• (00:00) - Introduction

• 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

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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.

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• (00:00) - Introduction

• 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?

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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.

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• (00:00) - Introduction

• 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

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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.

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

• 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

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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.
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• (00:00) - Introduction

• 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

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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.
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• (00:00) - Introduction

• 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

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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.
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• (00:00) - Introduction

• RecSys 2024 Conference Website

Papers:

• RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems

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