Original post

This is part 2 of a two-part series on recommendations using Dgraph. Check our part 1 here.
In the last post, we looked at how many applications and web apps no longer present static data, but rather generate interesting recommendations to users. There’s a whole field of theory and practice in recommendation engines that we touched on, talking about content-based (based on properties of objects) and collaborative (based on similar users) filtering techniques based on a chapter from Stanford MOOC Minning Massive Datasets.