# Building a Transformer-Based Category Recommender at Thumbtack

DevFeed: [Building a Transformer-Based Category Recommender at Thumbtack](<https://devfeed.tech/articles/building-a-transformer-based-category-recommender-at-thumbtack-24722.md>)

Original publisher: [Read original article](<https://medium.com/thumbtack-engineering/building-a-transformer-based-category-recommender-at-thumbtack-83636da91317?source=rss----1199c607a13f---4>)

Author: Andrew Morss

Published: 2026-07-20T20:23:27Z

Content type: article

Language: en

Sources: [Thumbtack Engineering - Medium](<https://devfeed.tech/sources/thumbtack-engineering-medium.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [email](<https://devfeed.tech/topics/email.md>), [push-notification](<https://devfeed.tech/topics/push-notification.md>)

Tags: [catalog](<https://devfeed.tech/tags/catalog.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [email](<https://devfeed.tech/tags/email.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [push-notification](<https://devfeed.tech/tags/push-notification.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [recommender-systems](<https://devfeed.tech/tags/recommender-systems.md>), [torch](<https://devfeed.tech/tags/torch.md>)

## AI overview

This engineering article describes Thumbtack's category recommender, which selects home-service categories for push and email notifications. It explains how the system addresses popularity bias through negative sampling and how Thumbtack's small category catalog and sparse user activity shape the recommendation problem.

## Source excerpt

A look at compensating for popularity bias in recommender systems using negative sampling strategies By: Andrew Morss, Senior Applied Scientist Introduction A recommender system is a machine learning model that, given a user and a catalog of items, predicts which items that user is most likely to want. Recommenders set your YouTube playlist, determine what items Amazon suggests for you, push you songs on Spotify and customize your Steam store. If you're a homeowner, Thumbtack's recommender systems can suggest home projects for you such as house cleaning or lawn mowing. Thumbtack connects users with professionals spread across almost 500 different job categories. Job categories are a kind of occupational bucket that Thumbtack uses as a taxonomy to categorize local service professionals (pros). Pros have the opportunity to choose categories of jobs that they want to receive leads on; examples include things like "Electrical and Wiring Repair", "Local Moving" or "Snow Plowing". Thumbtack's Lifecycle marketing team uses a category recommender to choose which categories to surface to users in our push and email notifications. Choosing the right ones is critical. A well-chosen suggestion is how a customer starts a search for a new project, finds the right service professional, and ultimately gets work done. An example of a push notification and an in-email category recommendation. The Problem Space: Category Recommendation There are several features of category recommendations at Thumbtack that are fairly unique in the item recommender space. The number of categories that we have is relatively small (~500) compared to the number of items that are usually seen in recommender systems, which simplifies the problem considerably. Usually recommenders require a two stage approach where an initial, low compute, filtering stage reduces the number of items to some "candidate" list of items before a more complicated model ranks that smaller set of items. With a relatively small cat