List: Recommendation | Curated by Shobeir Seddington

Dec 3, 2024

48 stories

Recommendation
In
tech-at-instacart
by
Ahsaas Bajaj
How Instacart Uses Machine Learning to Suggest Replacements for Out-of-Stock ProductsAuthors: Ahsaas Bajaj, Shishir Kumar Prasad
Nov 7, 2024
2
Nov 7, 2024
2
In
ProSiebenSat.1 Tech Blog
by
Simon Hawe
Video Recommendations at Joyn: Two Tower or Not to Tower, That Was Never a QuestionIn this article our Joyn team unravels the complexities of recommendation systems at our streaming platform Joyn.
Feb 15, 2024
3
Feb 15, 2024
3
 This story is no longer available
In
Pinterest Engineering Blog
by
Pinterest Engineering
NEP: Notification System and RelevanceLin Zhu; Staff Machine Learning Engineer | Eric Tam; Staff Software Engineer | Yuxiang Wang; Staff Machine Learning Engineer |
Aug 8, 2024
Aug 8, 2024
In
Intuitively and Exhaustively Explained
by
Daniel Warfield
CUDA for AI — Intuitively and Exhaustively ExplainedParallelized AI from scratch in CUDA
Jun 14, 2024
8
Jun 14, 2024
8
In
The Airbnb Tech Blog
by
Tiantian Zhang
Airbnb Brandometer: Powering Brand Perception Measurement on Social Media Data with AI
Apr 26, 2024
3
Apr 26, 2024
3
In
Pinterest Engineering Blog
by
Pinterest Engineering
Evolution of Ads Conversion Optimization Models at PinterestA Journey from GBDT to Multi-Task Ensemble DNN
Jan 9, 2024
Jan 9, 2024
In
Netflix TechBlog
by
Netflix Technology Blog
Causal Machine Learning for Creative InsightsA framework to identify the causal impact of successful visual components.
Jan 11, 2023
6
Jan 11, 2023
6
Netflix Technology Blog
Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation Systemby Roger Menezes, Rahul Jha, Gary Yeh, and Sudarshan Lamkhede
Aug 24, 2023
3
Aug 24, 2023
3
Michael Roizner
Counters in Recommendations: From Exponential Decay to Position DebiasingIn recommendations and similar ML tasks, the use of counters as features is a popular approach. Examples include the number of times a user…
Aug 16, 2023
1
Aug 16, 2023
1
In
Thumbtack Engineering
by
Derek Zhao
Accelerating Ranking Experimentation at Thumbtack with InterleavingCheck out how we measure user preference signals for search ranking problems
Jul 26, 2023
1
Jul 26, 2023
1
Rick Kosse
Building a Recommender System with Text Embeddings using Python and LightFMUnleashing the Power of Jokes…
Jun 19, 2023
Jun 19, 2023
Analytics at Meta
Four Analytics Best Practices We Adopted — and Why You should TooData-driven decisions are everywhere. Examples include launching a new feature in your favorite app, ensuring that the products do not…
Feb 28, 2023
2
Feb 28, 2023
2
In
tech-at-instacart
by
David Vengerov
Using Contextual Bandit models in large action spaces at InstacartDavid Vengerov, Vinesh Gudla, Tejaswi Tenneti, Haixun Wang, Kourosh Hakhamaneshi
Jun 15, 2023
Jun 15, 2023
In
Pinterest Engineering Blog
by
Pinterest Engineering
Deep Multi-task Learning and Real-time Personalization for Closeup RecommendationsHaomiao Li | Software Engineer, Closeup Ranking & Blending; Travis Ebesu | Software Engineer, Closeup Ranking & Blending; Fan Jiang |…
Jun 13, 2023
4
Jun 13, 2023
4
In
NVIDIA Merlin
by
Gabriel Moreira
Building ranking models powered by multi-task learning with Merlin and TensorFlowLarge online services like social media, streaming, e-commerce, and news provide a very broad catalog of items and leverage recommender…
Mar 13, 2023
1
Mar 13, 2023
1
In
Metarank
by
Roman Grebennikov
Search results diversification with MetarankA recent Metarank 0.6.3 release introduced a new diversity feature extractor, which measures how a particular item differs from others in…
Mar 7, 2023
Mar 7, 2023
In
Froomle
by
Robin Verachtert
What I Learned during 4 Years of Recommender Systems ResearchI’m currently in the process of writing my thesis, after four years of research into online recommender systems.
Here I’ll try to present…
Feb 21, 2023
Feb 21, 2023
In
Lyft Engineering
by
Jinshu Niu
The Recommendation System at LyftRecommendation plays an important role in Lyft’s understanding of its riders and allows for customizing app experiences to better fulfill…
Apr 3, 2023
1
Apr 3, 2023
1
Jo Kristian Bergum
Three mistakes when introducing embeddings and vector searchRepresenting unstructured data as embedding vectors and embedding-based retrieval (EBR) using vector search is more popular than ever. What…
Apr 14, 2023
9
Apr 14, 2023
9

Recommendation

How Instacart Uses Machine Learning to Suggest Replacements for Out-of-Stock Products

Authors: Ahsaas Bajaj, Shishir Kumar Prasad

Video Recommendations at Joyn: Two Tower or Not to Tower, That Was Never a Question

In this article our Joyn team unravels the complexities of recommendation systems at our streaming platform Joyn.

NEP: Notification System and Relevance

Lin Zhu; Staff Machine Learning Engineer | Eric Tam; Staff Software Engineer | Yuxiang Wang; Staff Machine Learning Engineer |

CUDA for AI — Intuitively and Exhaustively Explained

Parallelized AI from scratch in CUDA

Airbnb Brandometer: Powering Brand Perception Measurement on Social Media Data with AI

Evolution of Ads Conversion Optimization Models at Pinterest

A Journey from GBDT to Multi-Task Ensemble DNN

Causal Machine Learning for Creative Insights

A framework to identify the causal impact of successful visual components.

Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System

by Roger Menezes, Rahul Jha, Gary Yeh, and Sudarshan Lamkhede

Counters in Recommendations: From Exponential Decay to Position Debiasing

In recommendations and similar ML tasks, the use of counters as features is a popular approach. Examples include the number of times a user…

Accelerating Ranking Experimentation at Thumbtack with Interleaving

Check out how we measure user preference signals for search ranking problems

Building a Recommender System with Text Embeddings using Python and LightFM

Unleashing the Power of Jokes…

Four Analytics Best Practices We Adopted — and Why You should Too

Data-driven decisions are everywhere. Examples include launching a new feature in your favorite app, ensuring that the products do not…

Using Contextual Bandit models in large action spaces at Instacart

David Vengerov, Vinesh Gudla, Tejaswi Tenneti, Haixun Wang, Kourosh Hakhamaneshi

Deep Multi-task Learning and Real-time Personalization for Closeup Recommendations

Haomiao Li | Software Engineer, Closeup Ranking & Blending; Travis Ebesu | Software Engineer, Closeup Ranking & Blending; Fan Jiang |…

Building ranking models powered by multi-task learning with Merlin and TensorFlow

Large online services like social media, streaming, e-commerce, and news provide a very broad catalog of items and leverage recommender…

Search results diversification with Metarank

A recent Metarank 0.6.3 release introduced a new diversity feature extractor, which measures how a particular item differs from others in…

What I Learned during 4 Years of Recommender Systems Research

I’m currently in the process of writing my thesis, after four years of research into online recommender systems. Here I’ll try to present…

The Recommendation System at Lyft

Recommendation plays an important role in Lyft’s understanding of its riders and allows for customizing app experiences to better fulfill…

Three mistakes when introducing embeddings and vector search

Representing unstructured data as embedding vectors and embedding-based retrieval (EBR) using vector search is more popular than ever. What…

Shobeir Seddington