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…
#recommendation-system
8 posts
21 May
27 Apr
From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest
PinterestAuthors: 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…
13 Apr
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,…
7 Apr
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.…
28 Aug 2025
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.…
26 Aug 2025
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…
25 Aug 2025
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 —…
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…