Hi guys, im trying to understand how RecommenderJob works. Right now i was
thinking that was necessary choosing a particular similarity class like
Euclidean Distance and so on, so my algorithm could compute all similarities
for each pair of items and produce recommendations. Reading Mahout in Action,
"Distributing a Recommender" i have now some doubts about the correlation
between similarities like Euclidean, LogLike, Cosine and the co-occurence
matrix, as i see in RecommenderJob i can specify also "Co-occurrence" as a
similarity class, so what's the correct way to compute similarities and how
this happens with other similarities class and co-occurrence matrix/similarity.
Thank you very much for your further explanations :)
