State of the art advances in applied machine learning with a focus on recommender systems
Platform | Substack | Pricing | Only free issues | Publishes | Weekly |
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Issues | 35 | Subscribers | Read | recsysml.substack.com |
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Normally Value Models in recommender systems are hand tuned. We provide a couple of utilities to derive VM weights from targeted task importance.
We show the importance of calibration in ranking models and how to implement it efficiently.
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...
Are we looking at risk enough in recommender systems?
Other publications recommended by the authors of this newsletter.
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 = ❤️
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