Recsperts - Recommender Systems Experts

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 LinkedIn
• David Graus on Twitter
• David's Website
• RecSys in HR 2022: Workshop on Recommender Systems for Human Recources
• Randstad Annual Report 2021
• Talk 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 Gates
• Geyik et al. (2019): Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search

General Links:

• Follow me on Twitter: https://twitter.com/LivesInAnalogia
• Send me your comments, questions and suggestions to [email protected]
• Podcast 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 eight of Recsperts we discuss music recommender systems, the meaning of artist fairness and perspectives on recommender evaluation. I talk to Christine Bauer, who is an assistant professor at the University of Utrecht and co-organizer of the PERSPECTIVES workshop. Her research deals with context-aware recommender systems as well as the role of fairness in the music domain. Christine published work at many conferences like CHI, CHIIR, ICIS, and WWW.

In this episode we talk about the specifics of recommenders in the music streaming domain. In particular, we discuss the interests of different stakeholders, like users, the platform, or artists. Christine Bauer presents insights from her research on fairness with respect to the representation of artists and their interests. We talk about gender imbalance and how recommender systems could serve as a tool to counteract existing imbalances instead of reinforcing them, for example with simulations and reranking. In addition, we talk about the lack of multi-method evaluation and how open datasets incline researchers to focus too much on offline evaluation. In contrast, Christine argues for more user studies and online evaluation.

We wrap up with some final remarks on context-aware recommender systems and the potential of sensor data for improving context-aware personalization.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

Links from the Episode:

• Website of Christine Bauer
• Christine Bauer on LinkedIn
• Christine Bauer on Twitter
• PERSPECTIVES 2022: Perspectives on the Evaluation of Recommender Systems
• 5th FAccTRec Workshop: Responsible Recommendation

Papers:

• Ferraro et al. (2021): What is fair? Exploring the artists' perspective on the fairness of music streaming platforms
• Ferraro et. al (2021): Break the Loop: Gender Imbalance in Music Recommenders
• Jannach et al. (2020): Escaping the McNamara Fallacy: Towards More Impactful Recommender Systems Research
• Bauer et al. (2015): Designing a Music-controlled Running Application: a Sports Science and Psychological Perspective
• Dey et al. (2000): Towards a Better Understanding of Context and Context-Awareness

General Links:

• Follow me on Twitter: https://twitter.com/LivesInAnalogia
• Send me your comments, questions and suggestions to [email protected]
• Podcast Website: https://www.recsperts.com/
• (03:18) - Introducing Christine Bauer
• (09:08) - Multi-Stakeholder Interests in Music Recommender Systems
• (15:56) - Context-Aware Music Recommendations
• (21:55) - Fairness in Music RecSys
• (41:22) - Trade-Offs between Fairness and Relevance
• (48:18) - Evaluation Perspectives
• (01:02:37) - Further RecSys Challenges

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

• 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

General Links:

• Follow me on LinkedIn
• Follow me on X
• Send me your comments, questions and suggestions to [email protected]
• Recsperts 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

• 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

General Links:

• Follow me on LinkedIn
• Follow me on X
• Send me your comments, questions and suggestions to [email protected]
• 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.
Don't forget to follow the podcast and please leave a review

• (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
• Follow me on X
• Send me your comments, questions and suggestions to [email protected]
• Recsperts Website