Recsperts - Recommender Systems Experts

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/

A senior data scientist discusses podcast recommendations in the ARD Audiothek, exploring algorithms, mitigating bias, collaborative filtering, and content-based recommendations. He also talks about responsibility in providing diversified content suggestions and shares insights on becoming a novelist.

In this podcast, they discuss the importance of user modeling for recommendations and discovery, showcasing examples from YouTube's ad performance forecasting. They touch on real-time personalization and how Superlinked provides personalization as a service. The challenges of the RecSys community in rebranding for a better image are also highlighted.

Justin Basilico, director of research and engineering at Netflix, discusses the evolution of the Netflix recommender system from rating prediction to deep learning. They talk about the misalignment of metrics, the use of history, content, and context data, and the challenges of personalized page construction. They also touch on RecSysOps, cultural aspects at Netflix, and the importance of feedback and team collaboration.

In this episode of Recsperts we talk to Rishabh Mehrotra, the Director of Machine Learning at ShareChat, about users and creators in multi-stakeholder recommender systems. We learn more about users intents and needs, which brings us to the important matter of user satisfaction (and dissatisfaction). To draw conclusions about user satisfaction we have to perceive real-time user interaction data conditioned on user intents. We learn that relevance does not imply satisfaction as well as that diversity and discovery are two very different concepts.Rishabh takes us even further on his industry research journey where we also touch on relevance, fairness and satisfaction and how to balance them towards a fair marketplace. He introduces us into the creator economy of ShareChat. We discuss the post lifecycle of items as well as the right mixture of content and behavioral signals for generating recommendations that strike a balance between revenue and retention.In the end, we also conclude our interview with the benefits of end-to-end ownership and accountability in industrial RecSys work and how it makes people independent and effective. We receive some advice for how to grow and strive in tough job market times.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Chapters:(03:44) - Introduction Rishabh Mehrotra (19:09) - Ubiquity of Recommender Systems (23:32) - Moving from UCL to Spotify Research (33:17) - Moving from Research to Engineering (36:33) - Recommendations in a Marketplace (46:24) - Discovery vs. Diversity and Specialists vs. Generalists (55:24) - User Intent, Satisfaction and Relevant Recommendations (01:09:48) - Estimation of Satisfaction vs. Dissatisfaction (01:19:10) - RecSys Challenges at ShareChat (01:27:58) - Post Lifecycle and Mixing Content with Behavioral Signals (01:39:28) - Detect Fatigue and Contextual MABs for Ad Placement (01:47:24) - Unblock Yourself and Upskill (02:00:59) - RecSys Challenge 2023 by ShareChat (02:02:36) - Farewell Remarks Links from the Episode:Rishabh Mehrotra on LinkedinRishabh Mehrotra on TwitterRishabh's WebsitePapers:Mehrotra et al. (2017): Auditing Search Engines for Differential Satisfaction Across DemographicsMehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender SystemsMehrotra et al. (2019): Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate RecommendationsAnderson et al. (2020): Algorithmic Effects on the Diversity of Consumption on SpotifyMehrotra et al. (2020): Bandit based Optimization of Multiple Objectives on a Music Streaming PlatformHansen et al. (2021): Shifting Consumption towards Diverse Content on Music Streaming PlatformsMehrotra (2021): Algorithmic Balancing of Familiarity, Similarity & Discovery in Music RecommendationsJeunen et al. (2022): Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved ConfoundersGeneral Links:Follow me on Twitter: https://twitter.com/LivesInAnalogiaSend me your comments, questions and suggestions to marcel@recsperts.comPodcast Website: https://www.recsperts.com/

In this episode of Recsperts we talk to Flavian Vasile about the work of his team at Criteo AI Lab on personalized advertising. We learn about the different stakeholders like advertisers, publishers, and users and the role of recommender systems in this marketplace environment. We learn more about the pros and cons of click versus conversion optimization and transition to econ(omic) reco(mmendations), a new approach to model the effect of a recommendations system on the users' decision making process. Economic theory plays an important role for this conceptual shift towards better recommender systems.In addition, we discuss generative recommenders as an approach to directly translate a user’s preference model into a textual and/or visual product recommendation. This can be used to spark product innovation and to potentially generate what users really want. Besides that, it also allows to provide recommendations from the existing item corpus.In the end, we catch up on additional real-world challenges like two-tower models and diversity in recommendations.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Chapters:(02:37) - Introduction Flavian Vasile (06:46) - Personalized Advertising at Criteo (18:29) - Moving from Click to Conversion optimization (23:04) - Econ(omic) Reco(mmendations) (41:56) - Generative Recommender Systems (01:04:03) - Additional Real-World Challenges in RecSys (01:08:00) - Final Remarks Links from the Episode:Flavian Vasile on LinkedInFlavian Vasile on TwitterModern Recommendation for Advanced Practitioners - Part I (2019)Modern Recommendation for Advanced Practitioners - Part II (2019)CONSEQUENCES+REVEAL Workshop at RecSys 2022: Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning for Recommender SystemsPapers:Heymann et al. (2022): Welfare-Optimized Recommender SystemsSamaran et al. (2021): What Users Want? WARHOL: A Generative Model for RecommendationBonner et al (2018): Causal Embeddings for RecommendationVasile et al. (2016): Meta-Prod2Vec: Product Embeddings Using Side-Information for RecommendationGeneral Links:Follow me on Twitter: https://twitter.com/LivesInAnalogiaSend me your comments, questions and suggestions to marcel@recsperts.comPodcast Website: https://www.recsperts.com/

In episode number ten of Recsperts I welcome David Graus who is the Data Science Chapter Lead at Randstad Groep Nederland, a global leader in providing Human Resource services. We talk about the role of recommender systems in the HR domain which includes vacancy recommendations for candidates, but also generating talent recommendations for recruiters at Randstad. We also learn which biases might have an influence when using recommenders for decision support in the recruiting process as well as how Randstad mitigates them.In this episode we learn more about another domain where recommender systems can serve humans by effective decision support: Human Resources. Here, everything is about job recommendations, matching candidates with vacancies, but also exploiting knowledge about career path to propose learning opportunities and assist with career development. David Graus leads those efforts at Randstad and has previously worked in the news recommendation domain after obtaining his PhD from the University of Amsterdam.We discuss the most recent contribution by Randstad on mitigating bias in candidate recommender systems by introducing fairness-oriented post- and preprocessing to a recommendation pipeline. We learn that one can maintain user satisfaction while improving fairness at the same time (demographic parity measuring gender balance in this case).David and I also touch on his engagement in co-organizing the RecSys in HR workshops since RecSys 2021.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Links from the Episode:David Graus on LinkedInDavid Graus on TwitterDavid's WebsiteRecSys in HR 2022: Workshop on Recommender Systems for Human RecourcesRandstad Annual Report 2021Talk by David Graus at Anti-Discrimination Hackaton on "Algorithmic matching, bias, and bias mitigation"Papers:Arafan et al. (2022): End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness GatesGeyik et al. (2019): Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent SearchGeneral Links:Follow me on Twitter: https://twitter.com/LivesInAnalogiaSend me your comments, questions and suggestions to marcel@recsperts.comPodcast Website: https://www.recsperts.com/ (02:23) - Introduction David Graus (13:55) - About Randstad and the Staffing Industry (17:09) - Use Cases for RecSys Application in HR (22:04) - Talent and Vacancy Recommender System (33:46) - RecSys in HR Workshop (38:48) - Fairness for RecSys in HR (52:40) - Other HR RecSys Challenges (56:40) - Further RecSys Challenges

In episode number nine of Recsperts we talk with the creators of RecPack which is a new Python package for recommender systems. We discuss how Froomle provides modularized personalization for customers in the news and e-commerce sectors. I talk to Lien Michiels and Robin Verachtert who are both industrial PhD students at the University of Antwerp and who work for Froomle. We also hear about their research on filter bubbles as well as model drift along with their RecSys 2022 contributions.In this episode we introduce RecPack as a new recommender package that is easy to use and to extend and which allows for consistent experimentation. Lien and Robin share with us how RecPack evolved, its structure as well as the problems in research and practice they intend to solve with their open source contribution.My guests also share many insights from their work at Froomle where they focus on modularized personalization with more than 60 recommendation scenarios and how they integrate these with their customers. We touch on topics like model drift and the need for frequent retraining as well as on the tradeoffs between accuracy, cost, and timeliness in production recommender systems.In the end we also exchange about Lien's critical reception of using the term 'filter bubble', an operationalized definition of them as well as Robin's research on model degradation and training data selection.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Links from the Episode:Lien Michiels on LinkedInLien Michiels on TwitterRobin Verachtert on LinkedInRecPack on GitLabRecPack DocumentationFROOMLEPERSPECTIVES 2022: Perspectives on the Evaluation of Recommender SystemsPERSPECTIVES 2022: Preview on "Towards a Broader Perspective in Recommender Evaluation" by Benedikt Loepp5th FAccTRec Workshop: Responsible RecommendationPapers:Verachtert et al. (2022): Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training WindowLeysen and Michiels et al. (2022): What Are Filter Bubbles Really? A Review of the Conceptual and Empirical WorkMichiels and Verachtert et al. (2022): RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback DataDahlgren (2021): A critical review of filter bubbles and a comparison with selective exposureGeneral Links:Follow me on Twitter: https://twitter.com/LivesInAnalogiaSend me your comments, questions and suggestions to marcel@recsperts.comPodcast Website: https://www.recsperts.com/ (03:23) - Introduction Lien Michiels (07:01) - Introduction Robin Verachtert (09:29) - RecPack - Python Recommender Package (52:31) - Modularized Personalization in News and E-commerce by Froomle (01:09:54) - Research on Model Drift and Filter Bubbles (01:18:07) - Closing Questions