- [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.