Recommender

I use [Plex](plex.tv) to manage and stream all of the movies, tv shows, and music I consume. Many people use Netflix, but I have 12 TB of files that are **definitely not pirated**. Since I have such a massive amount of data, it is hard to find something new to watch.  

Plex recently introduced a recommendation engine. I imagine it works by looking at the SQL database it uses to store the data. Since the software auto-magically collects metadata from IMBD/TVDB/MusicBrainz and does sophisticated file analysis (detects bitrate, for example), it already has a ton of information about the kinds of show each user likes to watch. I know about their architecture because their source code is [here](https://github.com/plexinc), which itself is a fork of a firmware hack from the original Xbox.  

The likely use a bayesian classifier as it that model works well with the data model they use. Other possibilities include nearest neighbor classifiers that use a variety of genre/director/plot tags, random forest classifiers, and convolutional neural networks that Netflix uses to distill features like 'gritty'. Since Plex is a team of <10 people and mostly exists to serve pirates, I imagine this system is fairly simple. It's possible that it mines this data from IMDB, but I would be surprised and disappointed. The simplest implementation is probably good enough, but Netflix's complicated multi-layer classification network is far more engaging. However, Plex is designed to give me access to my content easily whereas Netflix wants me addicted to their platform (Plex is a software company; Netflix is becoming a production company). I'm not sure the more complicated models are better as each type has its benefits.