Describes how broad recommendations produced by a recommender system For example, a recommender that suggests only next book in series is less diverse than a recommender that can recommend books from other series To calculate diversity:

  1. Calculate similarity between items
  2. Calculate average similarity between recommendations in top-n list ()
  3. Diversity is opposite of similarity, so to get it subtract from ()

Unusually high diversity could mean bad recommendations