Introduction to Recommendations with Map-Reduce and mrjob by Marcel Caraciolo
From the post:
In this post I will present how can we use map-reduce programming model for making recommendations. Recommender systems are quite popular among shopping sites and social network thee days. How do they do it ? Generally, the user interaction data available from items and products in shopping sites and social networks are enough information to build a recommendation engine using classic techniques such as Collaborative Filtering.
Usual recommendation post except for the emphasis on multiple tests of similarity.
Useful because simply reporting that two (or more) items are “similar” isn’t all that helpful. At least unless or until you know the basis for the comparison.
And have the expectation that a similar notion of “similarity” works for your audience.
For example, I read an article this morning about a “new” invention that will change the face of sheet music publishing, in three to five years. Invention Will Strike a Chord With Musicians
Despite the lack of terms like “markup,” “HyTime,” “SGML,” “XML,” “Music Encoding Initiative (MEI),” or “MusicXML,” all of those seemed quite “similar” to me. That may not be the “typical” experience but it is mine.
If you don’t want to wait three to five years for the sheet music revolution, you can check out MusicXML. It has been reported that more than 150 applications support MusicXML. Oh, that would be today, not three to five years from now.
You might want to pass the word along in the music industry before the next “revolution” in sheet music starts up.