Matching Restaurant Menus to Crowdsourced Food Data, A Scalable Machine Learning Approach
Hesam Salehian (Under Armour);Chul Lee (Under Armour);Patrick Howell (Under Armour)
We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing. At first glance, this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches. However, due to the unique nature of our scenario and the need for scalability, our problem imposes certain restrictions on possible machine learning approaches that we can employ. We propose a novel, practical, and scalable machine learning solution architecture, consisting of two major steps. First we use a query generation approach, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to meet our required matching quality goals. It is important to note that our proposed solution architecture has already been deployed in a real application system serving tens of millions of users, and shows great potential for practical cases of user-entered text to structured text matching, especially when scalability is crucial.