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 WebsitePapers:RecSys '24: Proceedings of the 18th ACM Conference on Recommender SystemsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

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 LinkedInAmey's WebsiteRecSys Challenge 2021VideoRecSys Workshop 2023VideoRecSys + LargeRecSys 2024Papers:Mahajan et al. (2023): CAViaR: Context Aware Video RecommendationsMahajan et al. (2023): PIE: Personalized Interest Exploration for Large-Scale Recommender SystemsRaul et al. (2023): CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender SystemsZhai et al. (2024): Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative RecommendationsSaket et al. (2023): Formulating Video Watch Success Signals for Recommendations on Short Video PlatformsWang et al. (2022): Surrogate for Long-Term User Experience in Recommender SystemsSu et al. (2024): Long-Term Value of Exploration: Measurements, Findings and AlgorithmsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

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 LinkedInYashar's WebsiteKDD 2024 Tutorial: Modern Recommender Systems Leveraging Generative AI: Fundamentals, Challenges and OpportunitiesRecSys 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 ContentDeldjoo et al. (2021): A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial NetworksDeldjoo et al. (2020): How Dataset Characteristics Affect the Robustness of Collaborative Recommendation ModelsLiang et al. (2018): Variational Autoencoders for Collaborative FilteringHe et al. (2016): Visual Bayesian Personalized Ranking from Implicit FeedbackGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

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.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:51) - Guest Introductions (09:57) - Pinterest Introduction (21:57) - Homefeed Personalization (47:27) - Ads Ranking (01:14:58) - RecSys Challenge 2023 (01:20:26) - Closing Remarks Links from the Episode:Prabhat Agarwal on LinkedInAayush Mudgal on LinkedInRecSys Challenge 2023Pinterest Engineering BlogPinterest LabsPrabhat's Talk at GTC 2022: Evolution of web-scale engagement modeling at PinterestBlogpost: How we use AutoML, Multi-task learning and Multi-tower models for Pinterest AdsBlogpost: Pinterest Home Feed Unified Lightweight Scoring: A Two-tower ApproachBlogpost: Experiment without the wait: Speeding up the iteration cycle with Offline Replay ExperimentationBlogpost: MLEnv: Standardizing ML at Pinterest Under One ML Engine to Accelerate InnovationBlogpost: Handling Online-Offline Discrepancy in Pinterest Ads Ranking SystemPapers:Eksombatchai et al. (2018): Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-TimeYing et al. (2018): Graph Convolutional Neural Networks for Web-Scale Recommender SystemsPal et al. (2020): PinnerSage: Multi-Modal User Embedding Framework for Recommendations at PinterestPancha et al. (2022): PinnerFormer: Sequence Modeling for User Representation at PinterestZhao et al. (2019): Recommending what video to watch next: a multitask ranking systemGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Martijn Willemsen, expert in interactive recommender systems, discusses empowering users with control over recommendations, understanding user goals for better satisfaction, and the psychology of decision-making in recommendation systems. They explore music recommender systems, nudging users towards new genres, and the value of user feedback for improved recommendations.

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. Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (02:58) - About Bram van den Akker (09:16) - Motivation for Practical Bandits Tutorial (16:53) - Specifics and Challenges of Travel Recommendations (26:19) - Role of Bandit Feedback in Practice (49:13) - Motivation for Bandit Feedback (01:00:54) - Practical Start for Counterfactual Evaluation (01:06:33) - Role of Business Rules (01:11:26) - better cut this section coherently (01:17:48) - Rewards and More (01:32:45) - Closing Remarks Links from the Episode:Bram van den Akker on LinkedInPractical Bandits: An Industry Perspective (Website)Practical Bandits: An Industry Perspective (Recording)Tutorial at The Web Conference 2020: Unbiased Learning to Rank: Counterfactual and Online ApproachesTutorial at RecSys 2021: Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent AdvancesGitHub: Open Bandit PipelinePapers:van den Akker et al. (2023): Practical Bandits: An Industry Perspectivevan den Akker et al. (2022): Extending Open Bandit Pipeline to Simulate Industry Challengesvan den Akker et al. (2019): ViTOR: Learning to Rank Webpages Based on Visual FeaturesGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Himan Abdollahpouri, Applied Research Scientist at Spotify, delves into popularity bias in recommender systems. Topics include unfair recommendations for stakeholders, challenges in music and podcast streaming personalization, and strategies to counteract popularity bias. Learn about debiasing data, models, and outputs, as well as the relationship between multi-objective and multi-stakeholder recommender systems.

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.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (04:56) - About Sole Pera (06:37) - Non-traditional Populations (09:13) - Dedicated User Modeling (25:01) - Main Application Domains (40:16) - Lack of Data about non-traditional Populations (47:53) - Data for Learning User Profiles (57:09) - Interaction between Children and Recommendations (01:00:26) - Goals and Stakeholders (01:11:35) - Role of Parents and Trust (01:17:59) - Evaluation (01:26:59) - Research Community (01:32:37) - Closing Remarks Links from the Episode:Sole Pera on LinkedInSole's WebsiteChildren and RecommendersKidRec 2022People and Information Retrieval Team (PIReT)Papers:Beyhan et al. (2023): Covering Covers: Characterization Of Visual Elements Regarding SleevesMurgia et al. (2019): The Seven Layers of Complexity of Recommender Systems for Children in Educational ContextsPera et al. (2019): With a Little Help from My Friends: User of Recommendations at SchoolCharisi et al. (2022): Artificial Intelligence and the Rights of the Child: Towards an Integrated Agenda for Research and PolicyGómez et al. (2021): Evaluating recommender systems with and for children: towards a multi-perspective frameworkNg et al. (2018): Recommending social-interactive games for adults with autism spectrum disorders (ASD)General Links:Follow me on LinkedInFollow me on TwitterSend me your comments, questions and suggestions to marcel@recsperts.comRecsperts Website

Miguel Fierro, a Principal Data Science Manager at Microsoft with a PhD in robotics, dives deep into Microsoft's open-source recommenders repository, which boasts over 15k stars. He reveals how he transitioned from robotics to personalization, explaining the critical components of the system: examples, library, and tests. The conversation also explores the transformative impact of LLMs on recommender systems and emphasizes the ethical challenges and biases that must be addressed. Fierro concludes with insights on being a T-shaped data professional to thrive in a competitive landscape.

In episode 16 of Recsperts, we hear from Michael D. Ekstrand, Associate Professor at Boise State University, about fairness in recommender systems. We discuss why fairness matters and provide an overview of the multidimensional fairness-aware RecSys landscape. Furthermore, we talk about tradeoffs, methods and receive practical advice on how to get started with tackling unfairness.In our discussion, Michael outlines the difference and similarity between fairness and bias. We discuss several stages at which biases can enter the system as well as how bias can indeed support mitigating unfairness. We also cover the perspectives of different stakeholders with respect to fairness. We also learn that measuring fairness depends on the specific fairness concern one is interested in and that solving fairness universally is highly unlikely.Towards the end of the episode, we take a look at further challenges as well as how and where the upcoming RecSys 2023 provides a forum for those interested in fairness-aware recommender systems.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.(00:00) - Episode Overview (02:57) - Introduction Michael Ekstrand (17:08) - Motivation for Fairness-Aware Recommender Systems (25:45) - Overview and Definition of Fairness in RecSys (46:51) - Distributional and Representational Harm (53:59) - Relationship between Fairness and Bias (01:04:43) - Tradeoffs (01:13:36) - Methods and Metrics for Fairness (01:28:06) - Practical Advice for Tackling Unfairness (01:32:24) - Further Challenges (01:35:24) - RecSys 2023 (01:38:29) - Closing Remarks Links from the Episode:Michael Ekstrand on LinkedInMichael Ekstrand on MastodonMichael's WebsiteGroupLens Lab at University of MinnesotaPeople and Information Research Team (PIReT)6th FAccTRec Workshop: Responsible RecommendationNORMalize: The First Workshop on Normative Design and Evaluation of Recommender SystemsACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)Coursera: Recommender Systems SpecializationLensKit: Python Tools for Recommender SystemsChris Anderson - The Long Tail: Why the Future of Business Is Selling Less of MoreFairness in Recommender Systems (in Recommender Systems Handbook)Ekstrand et al. (2022): Fairness in Information Access SystemsKeynote at EvalRS (CIKM 2022): Do You Want To Hunt A Kraken? Mapping and Expanding Recommendation FairnessFriedler et al. (2021): The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision MakingSafiya Umoja Noble (2018): Algorithms of Oppression: How Search Engines Reinforce RacismPapers:Ekstrand et al. (2018): Exploring author gender in book rating and recommendationEkstrand et al. (2014): User perception of differences in recommender algorithmsSelbst et al. (2019): Fairness and Abstraction in Sociotechnical SystemsPinney et al. (2023): Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information AccessDiaz et al. (2020): Evaluating Stochastic Rankings with Expected ExposureRaj et al. (2022): Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine ResponsesMitchell et al. (2021): Algorithmic Fairness: Choices, Assumptions, and DefinitionsMehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender SystemsRaj et al. (2022): Measuring Fairness in Ranked Results: An Analytical and Empirical ComparisonBeutel et al. (2019): Fairness in Recommendation Ranking through Pairwise ComparisonsBeutel et al. (2017): Data Decisions and Theoretical Implications when Adversarially Learning Fair RepresentationsDwork et al. (2018): Fairness Under CompositionBower et al. (2022): Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender SystemsZehlike et al. (2022): Fairness in Ranking: A SurveyHoffmann (2019): Where fairness fails: data, algorithms, and the limits of antidiscrimination discourseSweeney (2013): Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertisingWang et al. (2021): User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided MarketsGeneral Links:Follow me on Twitter: https://twitter.com/MarcelKurovskiSend me your comments, questions and suggestions to marcel@recsperts.comPodcast Website: https://www.recsperts.com/