OneRec has established a blueprint for Generative Video Recs. This post shows what to change for Generative Friend Recommendations
Posts (24)
| Date | Post | Archived |
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| 2025-08-29 |
Building Generative Friend Recommendations
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| 2025-08-24 |
Using RL to maximize ad revenue without retention tradeoffs
Using Contextual Bandits and Predictive Modeling to Optimize Personalized Ad-Placement Policies
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| 2025-06-05 |
How to implement Generative Retrieval
GenAI meets recommender systems
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| 2025-05-25 |
Attention Explained: When to use Self, Graph, and Target-Aware Attention
Unlocking the Power of AI: A Beginner's Guide to Attention Architectures
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| 2025-05-17 |
Scalable Embedding based retrieval for target side value
Addressing Scalability Challenges in Two-Sided Embedding based Recommendations
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| 2024-11-24 |
Friend Recommendation Retrieval in a social network
From Graph Search to Deep Neural Two-Tower Models
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| 2024-09-10 |
Declarative Value-Model Tuning
Code to show a couple of approaches to achieve the desired task importance in value model
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| 2024-06-09 |
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…
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| 2024-05-24 |
Entrypoint retention modeling in recommender systems
Choose/rank items at the entrypoint of a recommended feed to drive retention and not just consumption
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| 2024-05-11 |
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.
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| 2024-05-03 |
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
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| 2024-01-20 |
Reducing selection bias / popularity bias in ranking
Through the post, code and videos we show how to make your multi-task ranking model unbiased
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| 2024-01-06 |
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
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| 2023-12-22 |
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
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| 2023-12-15 |
System design of an Early Ranker
Towards greater user satisfaction, recommendation quality, reduced latency and compute capacity savings + Sample code
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| 2023-11-26 |
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.
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| 2022-08-26 |
Using ML to build a social networking notification system
System design of an ML driven notification system of a social network
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| 2022-07-30 |
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
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| 2022-06-24 |
How to build ML-first products
Problems companies face in adopting ML and how to solve them
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| 2022-05-03 |
Fairness in ranking
Helping Elon build a "fair" ranking algorithm
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| 2021-12-05 |
Old-Search ~ training wheels of Neural-Search
Why it might be time to be semantic-first and not lexical-first in building search systems
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| 2021-10-14 |
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
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| 2021-09-17 |
Personalized short-video recommender systems
To recap the previous two articles in this short-video recommendation series, part-1 "Using online learning in short-video recommendations” showed a…
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| 2021-09-11 |
ML Design Interview | Design a short-video platform Part 2
Applied ML #16 | Part 2 ML Design of a short-video recommendation platform
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