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Applied ML | Recommender systems

Gaurav Chakravorty, Hong Chen, Saurabh Gupta, Vish Sangale, Nimit Desai, Marc Ferradou, Benjamin, Penghao Xu

State of the art advances in applied machine learning with a focus on recommender systems

Platform
Substack
PricingOnly free issuesPublishesWeekly
Issues35SubscribersRead 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.

2 months ago
7
2

Ranking model calibration in recommender systems

We show the importance of calibration in ranking models and how to implement it efficiently.

5 months ago
15
2

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...

5 months ago
5
0

Optimal whole page ranking = reward / risk

Are we looking at risk enough in recommender systems?

6 months ago
5
7

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Authors

The writers behind this newsletter.

  • Gaurav Chakravorty

    - Applied ML in Recommender systems (Facebook / Instagram, Google, Discord) - 20 years in Applied ML

  • Hong Chen

    An ML practitioner

  • Saurabh Gupta

    ML and AI practitioner for over a decade. Expertise in large scale recommender system

  • Vish Sangale

    AI ML

  • Nimit Desai

    Tech Lead / Engineering Manager for ML at Meta, with focus on core modeling for Recommendation systems.

  • Marc Ferradou

    Recsys + neovim = ❤️

  • Benjamin
  • Penghao Xu
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