To quickly create a client, I decided to use Angular CLI. Also, having the ability to know the current customer avoids the need to simply pick a customer at random. The recommendations engine needed enhancements to accept two additional forms of metadata:īy knowing the currently selected artist, the recommendations engine would know to exclude any recommendations for the same artist. While it would have been easy to update, the underlying schema did not have to change for this exercise and remained as shown below: Additionally, I felt like I needed to expand the number of artists used for this second example.Īs a result, I purged the data in my existing Slash GraphQL database and started over.
While the original article showed how the recommendations engine would work, I felt like my original data sample provided too many ratings for a few of the sample customers. This article will take things a little further and introduce an Angular client to present this data in a way that's easier to consume.and hopefully appreciate.
The article concluded by providing RESTful URIs in the Spring Boot recommendation engine to provide recommendations from the data stored in the Dgraph Slash GraphQL SaaS service offering. In short, the Slope One Ratings Algorithm would be at the heart of the recommendations engine. The article also provided insight on how the Slope One family of collaborative filtering algorithms could be utilized to predict the level of product interest based upon existing ratings. And using Slash GraphQL allowed me to quickly get a fully managed GraphQL database up and running with minimal effort. In the " Building a Recommendations Engine Using Spring Boot and Slash GraphQL" article, the recently released Slash GraphQL hosted GraphQL backend by Dgraph was utilized as a system of record for a Java-based recommendations engine.Ī graph datastore is a great fit for a use case such as a recommendation engine, where the relationships between the data are just as important as the data itself.
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