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How to implement Generative Retrieval
GenAI meets recommender systems
Jun 5
•
Gaurav Chakravorty
and
Samson Komo
13
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How to implement Generative Retrieval
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4
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
Samson Komo
6
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Attention Explained: When to use Self, Graph, and Target-Aware Attention
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Scalable Embedding based retrieval for target side value
Addressing Scalability Challenges in Two-Sided Embedding based Recommendations
May 17
•
Gaurav Chakravorty
and
Ridwan Amure
9
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Scalable Embedding based retrieval for target side value
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Two tower models for retrieval of recommendations
Feb 19, 2021
•
Gaurav Chakravorty
17
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Two tower models for retrieval of recommendations
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1
Early (Stage) Ranking in recommender systems
Nov 26, 2023
•
Gaurav Chakravorty
23
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Early (Stage) Ranking in recommender systems
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3
How to build ML-first products
Jun 24, 2022
•
Gaurav Chakravorty
24
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How to build ML-first products
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Ranking model calibration in recommender systems
Jun 9, 2024
•
Gaurav Chakravorty
and
Marc Ferradou
20
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Ranking model calibration in recommender systems
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2
Recommendations using graph neural networks
Mar 7, 2021
•
Gaurav Chakravorty
10
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Recommendations using graph neural networks
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4
Personalized short-video recommender systems
Sep 17, 2021
•
Gaurav Chakravorty
8
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Personalized short-video recommender systems
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Friend Recommendation Retrieval in a social network
From Graph Search to Deep Neural Two-Tower Models
Nov 24, 2024
•
Gaurav Chakravorty
,
Parag Agrawal
, and
Andrew Dodd
16
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Friend Recommendation Retrieval in a social network
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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
,
Penghao Xu
, and
Benjamin
7
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Declarative Value-Model Tuning
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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…
Jun 9, 2024
•
Gaurav Chakravorty
and
Marc Ferradou
20
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Ranking model calibration in recommender systems
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2
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
,
Vish Sangale
, and
Nimit Desai
7
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Entrypoint retention modeling in recommender systems
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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
Vish Sangale
5
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Optimal whole page ranking = reward / risk
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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
May 3, 2024
•
Gaurav Chakravorty
,
Hong Chen
, and
Saurabh Gupta
15
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User representation in a recommender system | memorization vs generalization
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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
Jan 20, 2024
•
Gaurav Chakravorty
and
Ameya Raul
9
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Reducing selection bias / popularity bias in ranking
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Applied ML | Recommender systems
State of the art advances in applied machine learning with a focus on recommender systems
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