Content-based filtering is out of scope in the Mahout framework, mainly because it is up to you to decide how to define similar items. If we want to do a content-based item-item similarity, we need to implement our own ItemSimilarity. For instance, in our book's dataset, we might want to make up the following rule for book similarity:

We could now implement our own similarity measure as follows:

class MyItemSimilarity implements ItemSimilarity {
 ...
 public double itemSimilarity(long itemID1, long itemID2) {
  MyBook book1 = lookupMyBook (itemID1);
  MyBook book2 = lookupMyBook (itemID2);
  double similarity = 0.0;
  if (book1.getGenre().equals(book2.getGenre()) 
   similarity += 0.15;
  }
  if (book1.getAuthor().equals(book2. getAuthor ())) {
   similarity += 0.50;
  }
  return similarity;
 }
 ...
}

We then use this ItemSimilarity instead of something like LogLikelihoodSimilarity or other implementations with a GenericItemBasedRecommender. That's about it. This is as far as we have to go to perform content-based recommendation in the Mahout framework.

What we saw here is one of the simplest forms of content-based recommendation. Another approach could be to create a content-based profile of users, based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually-rated content vectors.