Archive - Applied ML | Recommender systems

How to implement Generative Retrieval

GenAI meets recommender systems

Jun 5 •

Gaurav Chakravorty

and

1:18:39

May 2025

Attention Explained: When to use Self, Graph, and Target-Aware Attention

Unlocking the Power of AI: A Beginner's Guide to Attention Architectures

May 25 •

Gaurav Chakravorty

and

Scalable Embedding based retrieval for target side value

Addressing Scalability Challenges in Two-Sided Embedding based Recommendations

May 17 •

Gaurav Chakravorty

and

November 2024

Friend Recommendation Retrieval in a social network

From Graph Search to Deep Neural Two-Tower Models

Nov 24, 2024 •

Gaurav Chakravorty

,

, and

September 2024

Declarative Value-Model Tuning

Code to show a couple of approaches to achieve the desired task importance in value model

Sep 10, 2024 •

Gaurav Chakravorty

,

, and

June 2024

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…

Jun 9, 2024 •

Gaurav Chakravorty

and

May 2024

Entrypoint retention modeling in recommender systems

Choose/rank items at the entrypoint of a recommended feed to drive retention and not just consumption

May 24, 2024 •

Gaurav Chakravorty

,

, and

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.

May 11, 2024 •

Gaurav Chakravorty

and

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

May 3, 2024 •

Gaurav Chakravorty

,

, and

January 2024

Reducing selection bias / popularity bias in ranking

Through the post, code and videos we show how to make your multi-task ranking model unbiased

Jan 20, 2024 •

Gaurav Chakravorty

and

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

Jan 6, 2024 •

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

Dec 22, 2023 •

Gaurav Chakravorty

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