Applied ML | Recommender systems

Home
Notes
Archive
About
1:18:39
How to implement Generative Retrieval
GenAI meets recommender systems
 • 
Gaurav Chakravorty
 and 
Samson Komo
4
Attention Explained: When to use Self, Graph, and Target-Aware Attention
Unlocking the Power of AI: A Beginner's Guide to Attention Architectures
 • 
Gaurav Chakravorty
 and 
Samson Komo
Scalable Embedding based retrieval for target side value
Addressing Scalability Challenges in Two-Sided Embedding based Recommendations
 • 
Gaurav Chakravorty
 and 
Ridwan Amure
Most Popular
View all
Two tower models for retrieval of recommendations
 • Gaurav Chakravorty
1
Early (Stage) Ranking in recommender systems
 • Gaurav Chakravorty
3
How to build ML-first products
 • Gaurav Chakravorty
Ranking model calibration in recommender systems
 • Gaurav Chakravorty and Marc Ferradou
2
Recommendations using graph neural networks
 • Gaurav Chakravorty
4
Personalized short-video recommender systems
 • Gaurav Chakravorty
Friend Recommendation Retrieval in a social network
From Graph Search to Deep Neural Two-Tower Models
 • 
Gaurav Chakravorty
, 
Parag Agrawal
, and 
Andrew Dodd
Declarative Value-Model Tuning
Code to show a couple of approaches to achieve the desired task importance in value model
 • 
Gaurav Chakravorty
, 
Penghao Xu
, and 
Benjamin
2
Ranking model calibration in recommender systems
We define calibration of ranking models in calibration, the benefit it can bring to prioritize calibration and how to achieve it without affecting…
 • 
Gaurav Chakravorty
 and 
Marc Ferradou
2
Entrypoint retention modeling in recommender systems
Choose/rank items at the entrypoint of a recommended feed to drive retention and not just consumption
 • 
Gaurav Chakravorty
, 
Vish Sangale
, and 
Nimit Desai
Optimal whole page ranking = reward / risk
We show how tech can learn from finance in using risk models for better feed construction of recommender systems.
 • 
Gaurav Chakravorty
 and 
Vish Sangale
7
User representation in a recommender system | memorization vs generalization
We look at memorization, generalization and mixture of representations based implementations for user preference representation in a recommender system
 • 
Gaurav Chakravorty
, 
Hong Chen
, and 
Saurabh Gupta
Reducing selection bias / popularity bias in ranking
Through the post, code and videos we show how to make your multi-task ranking model unbiased
 • 
Gaurav Chakravorty
 and 
Ameya Raul
Applied ML | Recommender systems
State of the art advances in applied machine learning with a focus on recommender systems

Applied ML | Recommender systems

AboutArchiveSitemap
© 2025 RecSysML Journal team
Privacy ∙ Terms ∙ Collection notice
Start writingGet the app
Substack is the home for great culture