Applied ML | Recommender systems
Gaurav Chakravorty, Hong Chen, Saurabh Gupta, Vish Sangale, Nimit Desai, Marc Ferradou, Benjamin, Penghao Xu, Parag Agrawal, Andrew DoddState of the art advances in applied machine learning with a focus on recommender systems
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 35 | Subscribers | Read | recsysml.substack.com |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
Declarative Value-Model Tuning
Normally Value Models in recommender systems are hand tuned. We provide a couple of utilities to derive VM weights from targeted task importance.
Ranking model calibration in recommender systems
We show the importance of calibration in ranking models and how to implement it efficiently.
Entrypoint retention modeling in recommender systems
In our previous post we shared our learnings on how to consider the risk of abandonment in a repeated recommender system. In this post, we focus on the initial recommendation, which we call the “entrypoint” item. We share our learnings on i...
Optimal whole page ranking = reward / risk
Are we looking at risk enough in recommender systems?
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Authors
The writers behind this newsletter.
- Applied ML in Recommender systems (Facebook / Instagram, Google, Discord) - 20 years in Applied ML
An ML practitioner
ML and AI practitioner for over a decade. Expertise in large scale recommender system
AI ML
Tech Lead / Engineering Manager for ML at Meta, with focus on core modeling for Recommendation systems.
Recsys + neovim = ❤️
Head of Network Growth AI at LinkedIn
I like turtles
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