Thumbtack helps customers search for the right local professionals to get projects done. Our search product collects project details from customers and matches them against preferences from professionals. Afterwards, our ranking algorithm displays the professionals most likely to result in a job well done. We tackle the search ranking problem by scoring professionals that match the customer’s requirements and then sorting them by score. Earlier this year, we changed our search ranking algorithm from a heuristic scoring system to a machine learning (ML) based scoring system. This change was very challenging but impactful. In this blog post, we’ll discuss why we wanted to transition our search ranking algorithm to use machine learning,
By: Ben Anderson & Xin Liu
When a customer posts a request on Thumbtack, we want to match them with the right professional for the job. When the marketplace was small, this was easy—just blast the request out to all of the pros in the request’s category and location. Today, with millions of requests a year and hundreds of thousands of active pros, we can’t rely on that simple algorithm anymore. The definition of “right” is no longer obvious—the pro and the customer each have their own preferences, and we need to balance how we benefit customers and pros to grow a healthy marketplace in the long run.