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Building a Recommendation Engine on Azure
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Overview4m 15s
7
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Overview
DifficultyIntermediate
Duration27m
Students112

Description

Building a Recommendation Engine on Azure is a course designed for teams interested in using artificial intelligence to add product recommendations to their websites.

A product recommendation engine is a valuable feature that helps drive sales on e-commerce sites. In this course, you will learn the essentials of building, deploying, and testing a recommendation engine on Microsoft Azure. You will also build skills to fine-tune a recommendation model and evaluate its effectiveness.

This course is made up of five lectures covering deploying, testing, configuring, evaluating models, and making API requests. This is an intermediate level course, and prior Azure and API experience is recommended.

Learning Objectives

  • Deploy a recommendation engine on Microsoft Azure
  • Test and evaluate different recommendation models
  • Make API calls to the Microsoft Product Recommendations Solution

Intended Audience

  • People who are interested in artificial intelligence services on Microsoft Azure, especially recommendation engines

Prerequisites

  • Experience using Microsoft Azure
  • Experience using APIs

Related Training Content

Resources

The GitHub repository for this course is at https://github.com/cloudacademy/azure-recommendation-engine.

 

Transcript

- [Instructor] I hope you enjoyed learning about recommendation engines. Let's do a quick review of what you learned. First, you need to train a recommendation model using actual transactions from your product website. Collaborative filtering is a common algorithm used to make recommendations. It combines the preferences of many people to come up with a recommendation for a particular user. 

The Microsoft Product Recommendations Solution supports item to item and personalized recommendations. To provide a personalized recommendation, you need to give it a list of recent transactions for a given user. To get recommendations for products that don't have a sales history yet, you can add features to your products catalog. The algorithm will then figure out which other items are similar to this new product and base its recommendations on those similarities. The Similarity Function parameter can have a big effect on which items your model will recommend. To have it favor the most popular items, use Cooccurrence. 

To promote less popular items more often, use Lift. To strike a balance between the two, use Jaccard. Once you've implemented a recommendation engine, you can start gathering data on how users interacted with the recommendations, such as whether or not they clicked on them. Then you can feed this information into a new model to hopefully improve its recommendations. There are APIs available to perform all of the tasks related to the Recommendations Solution. 

One important part to remember is that you need to provide a key in the header of your API request. Otherwise, authorization will fail and so will the API request. Now you know how to deploy a recommendation engine on Azure, test and evaluate different recommendation models, and make API calls to the Microsoft Product Recommendations Solution. To learn more about Azure's AI services, you can read Microsoft's documentation. 

Also watch for new Microsoft Azure courses on Cloud Academy, because we're always publishing new courses. Please give this course a rating and if you have any questions or comments, please let us know. Thanks and keep on learning.

About the Author

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Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).