Recsperts - Recommender Systems Experts – Détails, épisodes et analyse

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.
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• (00:00) - Introduction
• (03:24) - About Robin Burke and First Recommender Systems
• (26:07) - From Fairness and Advertising to Multistakeholder RecSys
• (34:10) - Multistakeholder RecSys Terminology
• (40:16) - Multistakeholder vs. Multiobjective
• (42:43) - Reciprocal and Value-Aware RecSys
• (59:14) - Objective Integration vs. Reranking
• (01:06:31) - Social Choice for Recommendations under Fairness
• (01:17:40) - Post-Userist Recommender Systems
• (01:26:34) - Further Challenges and Closing Remarks

• Robin Burke on LinkedIn
• Robin's Website
• That Recommender Systems Lab
• Reference to Broder's Keynote on Computational Advertising and Recommender Systems from RecSys 2008
• Multistakeholder Recommender Systems (from Recommender Systems Handbook), chapter by Himan Abdollahpouri & Robin Burke
• POPROX: The Platform for OPen Recommendation and Online eXperimentation
• AltRecSys 2024 (Workshop at RecSys 2024)

Papers:

• Burke et al. (1996): Knowledge-Based Navigation of Complex Information Spaces
• Burke (2002): Hybrid Recommender Systems: Survey and Experiments
• Resnick et al. (1997): Recommender Systems
• Goldberg et al. (1992): Using collaborative filtering to weave an information tapestry
• Linden et al. (2003): Amazon.com Recommendations - Item-to-Item Collaborative Filtering
• Aird et al. (2024): Social Choice for Heterogeneous Fairness in Recommendation
• Aird et al. (2024): Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice
• Burke et al. (2024): Post-Userist Recommender Systems : A Manifesto
• Baumer et al. (2017): Post-userism
• Burke et al. (2024): Conducting Recommender Systems User Studies Using POPROX

General Links:

• Follow me on LinkedIn
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• Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
• Recsperts Website

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
• (03:10) - About Alessandro Piscopo and Duncan Walker
• (14:53) - RecSys Applications at the BBC
• (20:22) - Journey of Building Public Service Recommendations
• (28:02) - Role and Implementation of Public Service Values
• (36:52) - Algorithmic and Editorial Recommendation
• (01:01:54) - Further RecSys Challenges at the BBC
• (01:15:53) - Quare Workshop
• (01:23:27) - Closing Remarks

• Alessandro Piscopo on LinkedIn
• Duncan Walker on LinkedIn
• BBC
• QUARE @ RecSys 2023 (2nd Workshop on Measuring the Quality of Explanations in Recommender Systems)

Papers:

• Clarke et al. (2023): Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges
• Boididou et al. (2021): Building Public Service Recommenders: Logbook of a Journey
• Piscopo et al. (2019): Data-Driven Recommendations in a Public Service Organisation

General Links:

• Follow me on LinkedIn
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• Send me your comments, questions and suggestions to marcel.kurovski@gmail.com
• Recsperts Website

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.
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• (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

• 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)

General Links:

• Follow me on LinkedIn
• Follow me on Twitter
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Recsperts Website

In episode 17 of Recsperts, we meet Miguel Fierro who is a Principal Data Science Manager at Microsoft and holds a PhD in robotics. We talk about the Microsoft recommenders repository with over 15k stars on GitHub and discuss the impact of LLMs on RecSys. Miguel also shares his view of the T-shaped data scientist.

In our interview, Miguel shares how he transitioned from robotics into personalization as well as how the Microsoft recommenders repository started. We learn more about the three key components: examples, library, and tests. With more than 900 tests and more than 30 different algorithms, this library demonstrates a huge effort of open-source contribution and maintenance. We hear more about the principles that made this effort possible and successful. Therefore, Miguels also shares the reasoning behind evidence-based design to put the users of microsoft-recommenders and their expectations first. We also discuss the impact that recent LLM-related innovations have on RecSys.

At the end of the episode, Miguel explains the T-shaped data professional as an advice to stay competitive and build a champion data team. We conclude with some remarks regarding the adoption and ethical challenges recommender systems pose and which need further attention.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
Don't forget to follow the podcast and please leave a review

• (00:00) - Episode Overview
• (03:34) - Introduction Miguel Fierro
• (16:19) - Microsoft Recommenders Repository
• (30:04) - Structure of MS Recommenders
• (34:16) - Contributors to MS Recommenders
• (37:10) - Scalability of MS Recommenders
• (39:32) - Impact of LLMs on RecSys
• (48:26) - T-shaped Data Professionals
• (53:29) - Further RecSys Challenges
• (59:28) - Closing Remarks

• Miguel Fierro on LinkedIn
• Miguel Fierro on Twitter
• Miguel's Website
• Microsoft Recommenders
• McKinsey (2013): How retailers can keep up with consumers
• Fortune (2012): Amazon's recommendation secret
• RecSys 2021 Keynote by Max Welling: Graph Neural Networks for Knowledge Representation and Recommendation

Papers:

• Geng et al. (2022): Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

General Links:

• Follow me on LinkedIn
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• Send me your comments, questions and suggestions to marcel@recsperts.com
• Recsperts Website

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

• Michael Ekstrand on LinkedIn
• Michael Ekstrand on Mastodon
• Michael's Website
• GroupLens Lab at University of Minnesota
• People and Information Research Team (PIReT)
• 6th FAccTRec Workshop: Responsible Recommendation
• NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems
• ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
• Coursera: Recommender Systems Specialization
• LensKit: Python Tools for Recommender Systems
• Chris Anderson - The Long Tail: Why the Future of Business Is Selling Less of More
• Fairness in Recommender Systems (in Recommender Systems Handbook)
• Ekstrand et al. (2022): Fairness in Information Access Systems
• Keynote at EvalRS (CIKM 2022): Do You Want To Hunt A Kraken? Mapping and Expanding Recommendation Fairness
• Friedler et al. (2021): The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making
• Safiya Umoja Noble (2018): Algorithms of Oppression: How Search Engines Reinforce Racism

Papers:

• Ekstrand et al. (2018): Exploring author gender in book rating and recommendation
• Ekstrand et al. (2014): User perception of differences in recommender algorithms
• Selbst et al. (2019): Fairness and Abstraction in Sociotechnical Systems
• Pinney et al. (2023): Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access
• Diaz et al. (2020): Evaluating Stochastic Rankings with Expected Exposure
• Raj et al. (2022): Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses
• Mitchell et al. (2021): Algorithmic Fairness: Choices, Assumptions, and Definitions
• Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
• Raj et al. (2022): Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison
• Beutel et al. (2019): Fairness in Recommendation Ranking through Pairwise Comparisons
• Beutel et al. (2017): Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
• Dwork et al. (2018): Fairness Under Composition
• Bower et al. (2022): Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
• Zehlike et al. (2022): Fairness in Ranking: A Survey
• Hoffmann (2019): Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
• Sweeney (2013): Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertising
• Wang et al. (2021): User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets

General Links:

• Follow me on Twitter: https://twitter.com/MarcelKurovski
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast Website: https://www.recsperts.com/

In episode 15 of Recsperts, we delve into podcast recommendations with senior data scientist, Mirza Klimenta. Mirza discusses his work on the ARD Audiothek, a public broadcaster of audio-on-demand content, where he is part of pub. Public Value Technologies, a subsidiary of the two regional public broadcasters BR and SWR.

We explore the use and potency of simple algorithms and ways to mitigate popularity bias in data and recommendations. We also cover collaborative filtering and various approaches for content-based podcast recommendations, drawing on Mirza's expertise in multidimensional scaling for graph drawings. Additionally, Mirza sheds light on the responsibility of a public broadcaster in providing diversified content recommendations.

Towards the end of the episode, Mirza shares personal insights on his side project of becoming a novelist. Tune in for an informative and engaging conversation.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

• (00:00) - Episode Overview
• (01:43) - Introduction Mirza Klimenta
• (08:06) - About ARD Audiothek
• (21:16) - Recommenders for the ARD Audiothek
• (30:03) - User Engagement and Feedback Signals
• (46:05) - Optimization beyond Accuracy
• (51:39) - Next RecSys Steps for the Audiothek
• (57:16) - Underserved User Groups
• (01:04:16) - Cold-Start Mitigation
• (01:05:06) - Diversity in Recommendations
• (01:07:50) - Further Challenges in RecSys
• (01:10:03) - Being a Novelist
• (01:16:07) - Closing Remarks

• Mirza Klimenta on LinkedIn
• ARD Audiothek
• pub. Public Value Technologies
• Implicit: Fast Collaborative Filtering for Implicit Datasets
• Fairness in Recommender Systems: How to Reduce the Popularity Bias

Papers:

• Steck (2019): Embarrasingly Shallow Auoencoders for Sparse Data
• Hu et al. (2008): Collaborative Filtering for Implicit Feedback Datasets
• Cer et al. (2018): Universal Sentence Encoder

General Links:

• Follow me on Twitter: https://twitter.com/MarcelKurovski
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast Website: https://www.recsperts.com/

In episode number 14 of Recsperts we talk to Daniel Svonava, CEO and Co-Founder of Superlinked, delivering user modeling infrastructure. In his former role he was a senior software engineer and tech lead at YouTube working on ad performance prediction and pricing.

We discuss the crucial role of user modeling for recommendations and discovery. Daniel presents two examples from YouTube’s ad performance forecasting to demonstrate the bandwidth of use cases for user modeling. We also discuss sources of information that fuel user models and additional personlization tasks that benefit from it like user onboarding. We learn that the tight combination of user modeling with (near) real-time updates is key to a sound personalized user experience.

Daniel also shares with us how Superlinked provides personalization as a service beyond ecommerce-centricity. Offering personalized recommendations of items and people across various industries and use cases is what sets Superlinked apart. In the end, we also touch on the major general challenge of the RecSys community which is rebranding in order to establish a more positive image of the field.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Chapters:

• (03:35) - Introduction Daniel Svonava
• (10:18) - Introduction to User Modeling
• (17:52) - User Modeling for YouTube Ads
• (35:43) - Real-Time Personalization
• (57:29) - ML Tooling for User Modeling and Real-Time Personalization
• (01:07:41) - Superlinked as a User Modeling Infrastructure
• (01:31:22) - Rebranding RecSys as Major Challenge
• (01:37:40) - Final Remarks

• Daniel Svonava on LinkedIn
• Daniel Svonava on Twitter
• Superlinked - User Modeling Infrastructure
• The 2023 MAD (Machine Learning, Artificial Intelligence, Data Science) Landscape
• Eric Ries: The Lean Startup
• Rob Fitzpatrick: The Mom Test

Papers:

• Liu et al. (2022): Monolith: Real Time Recommendation System With Collisionless Embedding Table
• RSPapers Collection

General Links:

• Follow me on Twitter: https://twitter.com/MarcelKurovski
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast Website: https://www.recsperts.com/

This episode of Recsperts features Justin Basilico who is director of research and engineering at Netflix. Justin leads the team that is in charge of creating a personalized homepage. We learn more about the evolution of the Netflix recommender system from rating prediction to using deep learning, contextual multi-armed bandits and reinforcement learning to perform personalized page construction. Deep content understanding drives the creation of useful groupings of videos to be shown in a personalized homepage.

Justin and I discuss the misalignment of metrics as just one out of many elements that is making personalization still “super hard”. We hear more about the journey of deep learning for recommender systems where real usefulness comes from taking advantage of the variety of data besides pure user-item interactions, i.e. histories, content, and context. We also briefly touch on RecSysOps for detecting, predicting, diagnosing and resolving issues in a large-scale recommender systems and how it helps to alleviate item cold-start.

In the end of this episode, we talk about the company culture at Netflix. Key elements are freedom and responsibility as well as providing context instead of exerting control. We hear that being really comfortable with feedback is important for high-performance people and teams.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Chapters:

• (03:13) - Introduction Justin Basilico
• (07:37) - Evolution of the Netflix Recommender System
• (22:28) - Page Construction of the Personalized Netflix Homepage
• (32:12) - Misalignment of Metrics
• (37:36) - Experience with Deep Learning for Recommender Systens
• (48:10) - RecSysOps for Issue Detection, Diagnosis and Response
• (55:38) - Bandits Recommender Systems
• (01:03:22) - The Netflix Culture
• (01:13:33) - Further Challenges
• (01:15:48) - RecSys 2023 Industry Track
• (01:17:25) - Closing Remarks

• Justin Basilico on Linkedin
• Justin Basilico on Twitter
• Netflix Research Publications
• The Netflix Tech Blog
• CONSEQUENCES+REVEAL Workshop at RecSys 2022
• Learning a Personalized Homepage (Alvino et al., 2015)
• Recent Trends in Personalization at Netflix (Basilico, 2021)
• RecSysOps: Best Practices for Operating a Large-Scale Recommender System (Saberian et al., 2022)
• Netflix Fourth Quarter 2022 Earnings Interview
• No Rules Rules - Netflix and the Culture of Reinvention (Hastings et al., 2020)
• Job Posting for Netflix' Recommendation Team

Papers:

• Steck et al. (2021): Deep Learning for Recommender Systems: A Netflix Case Study
• Steck et al. (2021): Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher
• More et al. (2019): Recap: Designing a more Efficient Estimator for Off-policy Evaluation in Bandits with Large Action Spaces
• Bhattacharya et al. (2022): Augmenting Netflix Search with In-Session Adapted Recommendations

General Links:

• Follow me on Twitter: https://twitter.com/MarcelKurovski
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast Website: https://www.recsperts.com/

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

• Rishabh Mehrotra on Linkedin
• Rishabh Mehrotra on Twitter
• Rishabh's Website

Papers:

• Mehrotra et al. (2017): Auditing Search Engines for Differential Satisfaction Across Demographics
• Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
• Mehrotra et al. (2019): Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate Recommendations
• Anderson et al. (2020): Algorithmic Effects on the Diversity of Consumption on Spotify
• Mehrotra et al. (2020): Bandit based Optimization of Multiple Objectives on a Music Streaming Platform
• Hansen et al. (2021): Shifting Consumption towards Diverse Content on Music Streaming Platforms
• Mehrotra (2021): Algorithmic Balancing of Familiarity, Similarity & Discovery in Music Recommendations
• Jeunen et al. (2022): Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders

General Links:

• Follow me on Twitter: https://twitter.com/LivesInAnalogia
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast 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

• Flavian Vasile on LinkedIn
• Flavian Vasile on Twitter
• Modern 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 Systems

Papers:

• Heymann et al. (2022): Welfare-Optimized Recommender Systems
• Samaran et al. (2021): What Users Want? WARHOL: A Generative Model for Recommendation
• Bonner et al (2018): Causal Embeddings for Recommendation
• Vasile et al. (2016): Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation

General Links:

• Follow me on Twitter: https://twitter.com/LivesInAnalogia
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Podcast Website: https://www.recsperts.com/