Training and Testing of Recommender Systems on Data Missing Not at Random

     This paper talks about how users rate only a small portion of available movies. Normally recommender systems tend to ignore the missing ratings and treating all of them with the same weight. Some systems even try to correct the ratings or guess what they should be.
     This paper tells how the missing ratings are not random and have meaning that should be taken into effect. They state that people only rate the movies that they like or know. It is very unlikely for someone to rate a movie they have never even heard of before. They use the example that fans of horror movies rate horror films and fans of romance movies rate romance movies.  It is very unlikely for fans of either side to rate something from the other side unless they were fans of both. The missing ratings are not random and have just as much meaning as the traditional ratings. This approach helps users create a better recommender system.

Thanks and Gig 'em,
-Devin

The above blog was written about:
Training and testing of recommender systems on data missing not at random
http://dl.acm.org/citation.cfm?id=1835804.1835895