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
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
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How to build ML-first products
 • Gaurav Chakravorty
Early (Stage) Ranking in recommender systems
 • Gaurav Chakravorty
2
Recommendations using graph neural networks
 • Gaurav Chakravorty
4
Two tower models for retrieval of recommendations
 • Gaurav Chakravorty
1
Personalized short-video recommender systems
 • Gaurav Chakravorty
Fairness in ranking
 • Gaurav Chakravorty
System design of an Early Ranker
Towards greater user satisfaction, recommendation quality, reduced latency and compute capacity savings + Sample code
 • 
Gaurav Chakravorty
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
Using ML to build a social networking notification system
System design of an ML driven notification system of a social network
 • 
Gaurav Chakravorty
Life of a search query
Presentation of how search is today and how that is very different from the mental model users have of search in early 2000s
 • 
Gaurav Chakravorty
How to build ML-first products
Problems companies face in adopting ML and how to solve them
 • 
Gaurav Chakravorty
Fairness in ranking
Helping Elon build a "fair" ranking algorithm
 • 
Gaurav Chakravorty
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
Applied ML | Recommender systems
State of the art advances in applied machine learning with a focus on recommender systems
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Applied ML | Recommender systems

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