{"id":7978,"date":"2019-09-12T05:55:18","date_gmt":"2019-09-12T12:55:18","guid":{"rendered":"http:\/\/www.microsoft.com\/garage-en-us\/?p=7978"},"modified":"2019-09-12T05:55:34","modified_gmt":"2019-09-12T12:55:34","slug":"build-by-garage-interns-find-the-best-movie-powered-by-the-microsoft-recommenders-collection","status":"publish","type":"post","link":"http://approjects.co.za/?big=en-us/garage\/blog\/2019\/09\/build-by-garage-interns-find-the-best-movie-powered-by-the-microsoft-recommenders-collection\/","title":{"rendered":"Built by Garage Interns, find the best movie, powered by the Microsoft Recommenders collection"},"content":{"rendered":"

Challenged with rethinking how to build a movie recommendation experience, a team of Garage interns based out of Cambridge, MA created a sample app and corresponding documentation that shows how to use recommendation algorithms in an app experience. Today, we\u2019re excited to share their project ahead of its debut at the RecSys\u201919 conference next week: Recommenders Engine Experience Layout, a Microsoft Garage project<\/a>. This work joins a collection of best practices and tools for recommendation engines available on a larger Recommenders GitHub. Explore both on the Recommenders GitHub repository<\/a> and Recommenders Engine Example Layout GitHub repository<\/a> respectively.<\/p>\n

Bringing recommendation tools to apps<\/h3>\n

\"RecommenderRecommenders Engine Example Layout, focuses on recommendation algorithm experiences that take place in apps and provides a detailed breakdown of how developers can leverage the work by the sponsoring team. The Azure AI Customer Advisory Team, or AzureCAT AI works with such customers as ASOS<\/a> to incorporate enhanced recommenders algorithms into their solutions. The team was inspired to partner with a team of Garage interns upon continued feedback that expanding on their popular Recommenders repository with a focus on apps would be helpful to customers who already have an app infrastructure.<\/p>\n

“The key thing we wanted to demonstrate out of this was showing the recommenders we have, in a real-world setting that’s relevant to apps,” shares Scott Graham the Senior Data Scientist on Azure AI CAT who oversaw the project.”Often when we work with customers, they already have complex infrastructure and want to see how they can incorporate these algorithms into an app. This was a great opportunity to illustrate and document this.”<\/p>\n

The Garage project documents how to build a sample app powered by the Recommenders algorithms, featuring the MovieLens dataset, one of the largest open source collections of movie ratings. Put by Bruce Gatete, Program Manager Intern for the project. “It provides an end-to-end demonstration of how developers can build fully function cross-platform applications that use these algorithms.”<\/p>\n

\"RecommenderSample app key features<\/strong><\/p>\n

The sample app developers can build includes a wide variety of features<\/a>, including:<\/p>\n