JavaScript Recommendation Engine with Machine Learning and Graph Databases

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We aim to deliver the most valuable information possible to our users when developing front-end applications. However, the first iteration of the available information rarely fits the bill. This talk will look at how we implemented movie recommendations by collaborating with a team of data scientists, ML experts, and client-side engineers and the many data changes involved. This project aims to show how valuable machine learning data is when paired with graph databases. We also wanted to show the pros and cons of integrating ML data compared to using built-in database functionality. We created a movie streaming website that offers a fully functional user experience, an in-browser graph visualizer, and seamlessly switches between recommendation methods.

Our stack includes: VueJS (Vuex & Cytoscape), ArangoDB, Docker, and Nginx. This talk will focus on the frontend features such as using Cytoscape to display ArangoDB graphs and how we implemented a panel-based UI to allow for displaying information for multiple different recommendation types. Finally, we will briefly touch on the ML methods used to generate these recommendations and how they improved the experience for the users.

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