I'm referring to the code in org.apache.mahout.cf.taste.hadoop.item . Same algorithm really, different implementation. The task remains to combine them and take the best parts of both. The major difference is that one does the final matrix / user-vector multiplication in a distributed way and the other doesn't, and the question is what runs faster and scales better.
On Sun, Mar 21, 2010 at 5:13 PM, Claudio Martella <claudio.marte...@tis.bz.it> wrote: > Would you tell more about this point? I'm looking at > trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/cooccurence > > and I can find only one co-occurrence-based recommender following the > path > ItemBigraGenerator,ItemSimilarityEstimator,UserItemJoiner,UserItemRecommender.
