#8: Music Recommender Systems, Fairness and Evaluation with Christine Bauer

08/15/22 • 70 min

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.

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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

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:

Papers:

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

Previous Episode

#7: Behavioral Testing with RecList for Recommenders with Jacopo Tagliabue

In episode number seven, we meet Jacopo Tagliabue and discuss behavioral testing for recommender systems and experiences from ecommerce. Before Jacopo became the director of artificial intelligence at Coveo, he had founded tooso, which was later acquired by Coveo. Jacopo holds a PhD in cognitive intelligence and made many contributions to conferences like SIGIR, WWW, or RecSys. In addition, he serves as adjunct professor at NYU.

In this episode we introduce behavioral testing for recommender systems and the corresponding framework RecList that was created by Jacopo and his co-authors. Behavioral testing goes beyond pure retrieval accuracy metrics and tries to uncover unintended behavior of recommender models. RecList is an adaption of CheckList that applies behavioral testing to NLP and which was proposed by Microsoft some time ago. RecList comes with an open-source framework with ready set datasets for different recommender use-cases like similar, sequence-based and complementary item recommendations. Furthermore, it offers some sample tests to make it easier for newcomers to get started with behavioral testing. We also briefly touch on the upcoming CIKM data challenge that is going to focus on the evaluation of recommender systems.

In the end of this episode Jacopo also shares his insights from years of building and using diverse ML Ops tools and talk about what he refers to as the "post-modern stack".

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

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Follow me on Twitter: https://twitter.com/LivesInAnalogia
Send me your comments, questions and suggestions to [email protected]
Podcast Website: https://www.recsperts.com/

Next Episode

#9: RecPack and Modularized Personalization by Froomle with Lien Michiels and Robin Verachtert

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.

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