#recommendation-system

Pinterest Engineering 27 Jul 2026 13 min read

Part 1 of 2 Authors Personalization (Homefeed): Yuke Yan, Chuxi Wang, Andreanne Lemay, Olafur Gudmundsson, Anna Kiyantseva, Krystal Benitez, Jongho Kim, Jiacong He, Rahul Goutam, James Li, Dylan Wang User Understanding: Simin Li, Sufyan Suliman, Yingjian Ding, Hongbo Deng Data Science: Armando Ordorica, Yan Chen, Ellie Zhang, Karim Wahba Introduction Pinterest’s mission is to help people discover the inspiration to…

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Daochen Zha 21 Jul 2026 8 min read

How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha , Chun How Tan , Xin Liu , Bin Xu , Han Zhao , Xiaowei Liu , Jun Shi , Tracy Yu , Hui Gao , Huiji Gao , Liwei He , Michael Kinoti ,…

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Netflix Technology Blog 29 Jun 2026 22 min read

Authors: Lequn Wang , J iangwei Pan , and Linas Baltrunas Figure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what’s already on the page and the user’s context. Introduction The Netflix homepage is the first thing users see when they open the app and the primary way…

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Pinterest Engineering 21 May 2026 12 min read

Authors ( listed alphabetically ) Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le Zhang Core ML Infra team: Eric Shang, Pihui Wei ML Data team: Connor Votroubek, Yi He User Understanding team: Camilo Munoz, Simin Li If you work on ranking, retrieval, or recommendation systems, you’ve probably asked for some version of the same thing: “Give me the last N…

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Pinterest Engineering 27 Apr 2026 7 min read

Authors: Richard Huang | Machine Learning Engineer II; Yu Liu | Senior Machine Learning Engineer; Ziwei Guo | Senior Machine Learning Engineer; Andy Mao | Staff Machine Learning Engineer; Supeng Ge | Sr. Staff Machine Learning Engineer Introduction At Pinterest, conversion ads are crucial for matching users with products they are likely to purchase, boosting value for both users and…

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Pinterest Engineering 13 Apr 2026 8 min read

Authors: Matt Lawhon | Sr. Machine Learning Engineer; Filip Ryzner | Machine Learning Engineer II; Kousik Rajesh | Machine Learning Engineer II; Chen Yang | Sr. Staff Machine Learning Engineer; Saurabh Vishwas Joshi | Principal Engineer At Pinterest, scaling our recommendation models delivers outsized impact on the quality of the content we serve to users. Our Foundation Model (oral spotlight,…

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Pinterest Engineering 7 Apr 2026 9 min read

Homefeed: Jiacong He, Dafang He, Jie Cheng (former), Andreanne Lemay, Mostafa Keikha, Rahul Goutam, Dhruvil Deven Badani, Dylan Wang Content Quality: Jianing Sun, Qinglong Zeng ML Serving: Li Tang Introduction In feed recommendation, we recommend a list of items for the user to consume. It’s typically handled separately from the ranking model where we give probability predictions of user-item pairs.…

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Raphael Montaud 28 Aug 2025 14 min read

How we made our filtering 10x cheaper by removing our Bloom Filters Bloom Filters are great tools to make fast and cheap filtering. They also come with plenty of problems and can easily get expensive and cumbersome. We switched to user-based direct database queries, which made our filtering cheaper and easy to maintain. Here’s the full breakdown of that migration.…

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Raphael Montaud 26 Aug 2025 7 min read

How we made our email story recommendations better In this Part 1, you’ll understand how we improved one of the main ways our users are exposed to our product and how that led to a massive 7% increase on the average reading time for the digest users. Intro : This is a 4-part series breaking down improvements to the algorithm…

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Raphael Montaud 25 Aug 2025 6 min read

Cross-Digest diversification In this part 4, we’ll see how we went from investigating a few complaints from digest power users to improving our digest recommendations across the board. Intro : This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past year. When we started this work, the Digest was suboptimal —…

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Raphael Montaud 25 Aug 2025 10 min read

Hard vs Soft Filtering and how this applies to Medium’s Recommendation System In this part 3 we’ll see how we modified one of our hard filtering rules and attempted to turn it into a machine learning based “soft filter”. Intro : This is a 4-part series breaking down improvements to the algorithm behind the Medium’s Daily Digest over the past…

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Juan Pablo Lorenzo 18 Jun 2025 7 min read

Unlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data Enrichments How are accurate property prices on Booking.com connected to machine learning that recommends appealing property photos? What about the number of users who have wishlisted a property? And how can developers assess if their recommendation models effectively boost traveler clicks? None of these pieces of information are…

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Deepak H R 14 Nov 2022 5 min read

Primary author: Deepak H R Project guidance: Narasimha M MakeMyTrip experiments with multiple ranking recommendation systems to measure offline metric improvement and maximize online business or engagement metric lift. Data science systems, for example, use collaborative filtering, learning-to-rank algorithms, attribution models, debiasing techniques, embedding representation learning methods, shallow GNN methods, content-based representations, and lightGBM to Deep neural network architectures.

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5 Jul 2016 3 min read

Over the last 100 years we have dialed into radio stations at home, on the road, or in the office to access a curated mix of top hits delivered to us by our favorite DJ. With more and more of our daily activities taking place online, we find our source of music now comes from a mix of our mobile…

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21 Jun 2016 3 min read

With more than 125 million tracks from over 12 million creators heard each month on our platform, SoundCloud is uniquely positioned to offer listeners a full spectrum of music discovery. Classic hits, the latest releases, gems from underground talent and the best of what’s up-and-coming – all in one place. How can you make great content discoverable and available at…

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