Applied ML | Recommender systems

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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
How to reduce cost of ranking by knowledge distillation
Using knowledge distillation can make your early ranking model more aligned with final ranker + Sample Code + Video walkthrough
 • 
Gaurav Chakravorty

December 2023

Does your model get better at task T when you rank by estimated probability p(T) ?
To understand what to optimize in a ranking model
 • 
Gaurav Chakravorty
System design of an Early Ranker
Towards greater user satisfaction, recommendation quality, reduced latency and compute capacity savings + Sample code
 • 
Gaurav Chakravorty

November 2023

Early (Stage) Ranking in recommender systems
We look at "early (stage) ranking", why it is needed, what is success for it and end with a baseline implementation of it.
 • 
Gaurav Chakravorty
2

August 2022

Using ML to build a social networking notification system
System design of an ML driven notification system of a social network
 • 
Gaurav Chakravorty

July 2022

June 2022

How to build ML-first products
Problems companies face in adopting ML and how to solve them
 • 
Gaurav Chakravorty

May 2022

Fairness in ranking
Helping Elon build a "fair" ranking algorithm
 • 
Gaurav Chakravorty

December 2021

Old-Search ~ training wheels of Neural-Search
Why it might be time to be semantic-first and not lexical-first in building search systems
 • 
Gaurav Chakravorty
1

October 2021

Logging | How to build self-learning search and recommender systems
Server side and client side logging needed to build recommender systems, ads and search systems
 • 
Gaurav Chakravorty

September 2021

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