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

09/15/22 • 87 min

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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Follow me on Twitter: https://twitter.com/LivesInAnalogia
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Podcast 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

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/
(03:23) - 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

Previous Episode

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

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:

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

Next Episode

#10: Recommender Systems in Human Resources with David Graus

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.

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