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