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.

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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.

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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.

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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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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.

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• (00:00) - Introduction

• 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

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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.

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• (00:00) - Introduction

• 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

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In episode 22 of Recsperts, we welcome Prabhat Agarwal, Senior ML Engineer, and Aayush Mudgal, Staff ML Engineer, both from Pinterest, to the show. Prabhat works on recommendations and search systems at Pinterest, leading representation learning efforts. Aayush is responsible for ads ranking and privacy-aware conversion modeling. We discuss user and content modeling, short- vs. long-term objectives, evaluation as well as multi-task learning and touch on counterfactual evaluation as well.

In our interview, Prabhat guides us through the journey of continuous improvements of Pinterest's Homefeed personalization starting with techniques such as gradient boosting over two-tower models to DCN and transformers. We discuss how to capture users' short- and long-term preferences through multiple embeddings and the role of candidate generators for content diversification. Prabhat shares some details about position debiasing and the challenges to facilitate exploration.
With Aayush we get the chance to dive into the specifics of ads ranking at Pinterest and he helps us to better understand how multifaceted ads can be. We learn more about the pain of having too many models and the Pinterest's efforts to consolidate the model landscape to improve infrastructural costs, maintainability, and efficiency. Aayush also shares some insights about exploration and corresponding randomization in the context of ads and how user behavior is very different between different kinds of ads.
Both guests highlight the role of counterfactual evaluation and its impact for faster experimentation.

Towards the end of the episode, we also touch a bit on learnings from last year's RecSys challenge.

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• (00:00) - Introduction

• Prabhat Agarwal on LinkedIn
• Aayush Mudgal on LinkedIn
• RecSys Challenge 2023
• Pinterest Engineering Blog
• Pinterest Labs
• Prabhat's Talk at GTC 2022: Evolution of web-scale engagement modeling at Pinterest
• Blogpost: How we use AutoML, Multi-task learning and Multi-tower models for Pinterest Ads
• Blogpost: Pinterest Home Feed Unified Lightweight Scoring: A Two-tower Approach
• Blogpost: Experiment without the wait: Speeding up the iteration cycle with Offline Replay Experimentation
• Blogpost: MLEnv: Standardizing ML at Pinterest Under One ML Engine to Accelerate Innovation
• Blogpost: Handling Online-Offline Discrepancy in Pinterest Ads Ranking System

Papers:

• Eksombatchai et al. (2018): Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
• Ying et al. (2018): Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Pal et al. (2020): PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
• Pancha et al. (2022): PinnerFormer: Sequence Modeling for User Representation at Pinterest
• Zhao et al. (2019): Recommending what video to watch next: a multitask ranking system

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In episode 21 of Recsperts, we welcome Martijn Willemsen, Associate Professor at the Jheronimus Academy of Data Science and Eindhoven University of Technology. Martijn's researches on interactive recommender systems which includes aspects of decision psychology and user-centric evaluation. We discuss how users gain control over recommendations, how to support their goals and needs as well as how the user-centric evaluation framework fits into all of this.

In our interview, Martijn outlines the reasons for providing users control over recommendations and how to holistically evaluate the satisfaction and usefulness of recommendations for users goals and needs. We discuss the psychology of decision making with respect to how well or not recommender systems support it. We also dive into music recommender systems and discuss how nudging users to explore new genres can work as well as how longitudinal studies in recommender systems research can advance insights.

Towards the end of the episode, Martijn and I also discuss some examples and the usefulness of enabling users to provide negative explicit feedback to the system.

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• (00:00) - Introduction

• Martijn Willemsen on LinkedIn
• Martijn Willemsen's Website
• User-centric Evaluation Framework
• Behaviorism is not Enough (Talk at RecSys 2016)
• Neil Hunt: Quantifying the Value of Better Recommendations (Keynote at RecSys 2014)
• What recommender systems can learn from decision psychology about preference elicitation and behavioral change (Talk at Boise State (Idaho) and Grouplens at University of Minnesota)
• Eric J. Johnson: The Elements of Choice
• Rasch Model
• Spotify Web API

Papers:

• Ekstrand et al. (2016): Behaviorism is not Enough: Better Recommendations Through Listening to Users
• Knijenburg et al. (2012): Explaining the user experience of recommender systems
• Ekstrand et al. (2014): User perception of differences in recommender algorithms
• Liang et al. (2022): Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
• McNee et al. (2006): Being accurate is not enough: how accuracy metrics have hurt recommender systems

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In episode 20 of Recsperts, we welcome Bram van den Akker, Senior Machine Learning Scientist at Booking.com. Bram's work focuses on bandit algorithms and counterfactual learning. He was one of the creators of the Practical Bandits tutorial at the World Wide Web conference. We talk about the role of bandit feedback in decision making systems and in specific for recommendations in the travel industry.

In our interview, Bram elaborates on bandit feedback and how it is used in practice. We discuss off-policy- and on-policy-bandits, and we learn that counterfactual evaluation is right for selecting the best model candidates for downstream A/B-testing, but not a replacement. We hear more about the practical challenges of bandit feedback, for example the difference between model scores and propensities, the role of stochasticity or the nitty-gritty details of reward signals. Bram also shares with us the challenges of recommendations in the travel domain, where he points out the sparsity of signals or the feedback delay.

At the end of the episode, we can both agree on a good example for a clickbait-heavy news service in our phones.

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• (00:00) - Introduction

• Bram van den Akker on LinkedIn
• Practical Bandits: An Industry Perspective (Website)
• Practical Bandits: An Industry Perspective (Recording)
• Tutorial at The Web Conference 2020: Unbiased Learning to Rank: Counterfactual and Online Approaches
• Tutorial at RecSys 2021: Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances
• GitHub: Open Bandit Pipeline

Papers:

• van den Akker et al. (2023): Practical Bandits: An Industry Perspective
• van den Akker et al. (2022): Extending Open Bandit Pipeline to Simulate Industry Challenges
• van den Akker et al. (2019): ViTOR: Learning to Rank Webpages Based on Visual Features

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In episode 19 of Recsperts, we welcome Himan Abdollahpouri who is an Applied Research Scientist for Personalization & Machine Learning at Spotify. We discuss the role of popularity bias in recommender systems which was the dissertation topic of Himan. We talk about multi-objective and multi-stakeholder recommender systems as well as the challenges of music and podcast streaming personalization at Spotify.

In our interview, Himan walks us through popularity bias as the main cause of unfair recommendations for multiple stakeholders. We discuss the consumer- and provider-side implications and how to evaluate popularity bias. Not the sheer existence of popularity bias is the major problem, but its propagation in various collaborative filtering algorithms. But we also learn how to counteract by debiasing the data, the model itself, or it's output. We also hear more about the relationship between multi-objective and multi-stakeholder recommender systems.

At the end of the episode, Himan also shares the influence of popularity bias in music and podcast streaming at Spotify as well as how calibration helps to better cater content to users' preferences.

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• (00:00) - Introduction

• Himan Abdollahpouri on LinkedIn
• Himan Abdollahpouri on X
• Himan's Website
• Himan's PhD Thesis on "Popularity Bias in Recommendation: A Multi-stakeholder Perspective"
• 2nd Workshop on Multi-Objective Recommender Systems (MORS @ RecSys 2022)

Papers:

• Su et al. (2009): A Survey on Collaborative Filtering Techniques
• Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
• Abdollahpouri et al. (2021): User-centered Evaluation of Popularity Bias in Recommender Systems
• Abdollahpouri et al. (2019): The Unfairness of Popularity Bias in Recommendation
• Abdollahpouri et al. (2017): Controlling Popularity Bias in Learning-to-Rank Recommendation
• Wasilewsi et al. (2016): Incorporating Diversity in a Learning to Rank Recommender System
• Oh et al. (2011): Novel Recommendation Based on Personal Popularity Tendency
• Steck (2018): Calibrated Recommendations
• Abdollahpouri et al. (2023): Calibrated Recommendations as a Minimum-Cost Flow Problem
• Seymen et al. (2022): Making smart recommendations for perishable and stockout products

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In episode 18 of Recsperts, we hear from Professor Sole Pera from Delft University of Technology. We discuss the use of recommender systems for non-traditional populations, with children in particular. Sole shares the specifics, surprises, and subtleties of her research on recommendations for children.

In our interview, Sole and I discuss use cases and domains which need particular attention with respect to non-traditional populations. Sole outlines some of the major challenges like lacking public datasets or multifaceted criteria for the suitability of recommendations. The highly dynamic needs and abilities of children pose proper user modeling as a crucial part in the design and development of recommender systems. We also touch on how children interact differently with recommender systems and learn that trust plays a major role here.

Towards the end of the episode, we revisit the different goals and stakeholders involved in recommendations for children, especially the role of parents. We close with an overview of the current research community.

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• (00:00) - Introduction

• Sole Pera on LinkedIn
• Sole's Website
• Children and Recommenders
• KidRec 2022
• People and Information Retrieval Team (PIReT)

Papers:

• Beyhan et al. (2023): Covering Covers: Characterization Of Visual Elements Regarding Sleeves
• Murgia et al. (2019): The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts
• Pera et al. (2019): With a Little Help from My Friends: User of Recommendations at School
• Charisi et al. (2022): Artificial Intelligence and the Rights of the Child: Towards an Integrated Agenda for Research and Policy
• Gómez et al. (2021): Evaluating recommender systems with and for children: towards a multi-perspective framework
• Ng et al. (2018): Recommending social-interactive games for adults with autism spectrum disorders (ASD)

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