title: Basic recommendation engine algorithm
body: <p>I'm looking to write a basic recommender system in Objective-C and I'm looking for a basic algorithm for the job. Unfortunately off-the-shelf systems are off the table since none seem to be for Objective-C.</p>
  
  <p>I'm going to have a database of items, each with tags (think movies with tags like "horror", "action", etc). Each item would have ~5 or so of these tags. When a user first uses the app their profile will be primed based on their input to a series of questions, associating some tags with their profile.</p>
  
  <p>As the user continues to use the system and rate various items (on a hate/like/love basis) I'd like to adjust the weighting of the recommended tags based on that feedback. I'd also like to take in a few other properties of their ratings as their profile grows, like for example "the 80s" if this dealt with movies. Or maybe director, sticking with the movie theme. </p>
  
  <p>I'm opting to avoid the normal (or at least popular) recommender systems where it looks for similar users to generate recommendations. This is going to have a <em>large</em> item database and minimal users to start.</p>
  
  <p>Can anyone recommend a good starting point for an algorithm like this, I'd hate to reinvent the wheel, and there's a lot out there?</p>