Hi
A few questions:
1. I see that one of the parameters of the distributed co-occurrence item
similarity is the name of the item similarity class.
I wonder why it is an option? The all idea behind this algorithm is that
the similarity is based on co-occurrences, what am I missing here?
2. If I want to use the distributed slope-one average diff job, but I do
not want to save all items per item (I want to avoid from saving an I*I
matrix), what should be the way I can filter the amount of items I'm saving
(such as I can do in the item based recommender)?
3. Related to Sean question a few days ago... If I want to simplify the
overhead in ALS prediction of doing Xu * Y' in order to get user u
recommendation, does something like saving for each of the K features only the
top K highest items could be a good heuristic? That way I'm reducing
dramatically the number of candidate items and they are the potential items to
get the highest score, I think...
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