What is the intuition regarding the choice or tuning of the ALS params?
Job-Specific Options:
--lambda lambda regularization
parameter
--implicitFeedback implicitFeedback data consists of implicit
feedback?
--alpha alpha confidence parameter
(only used on
implicit feedback)
--numFeatures numFeatures dimension of the feature space
--numIterations numIterations number of iterations
I've set up an iterative search for the lambda that gets the lowest rmse but
what is the likely range? Can the range to search be determined from the data
(all 1 or nothing in my case).
I do plan to include implicit feedback (values less than 1) eventually. Not
sure what this controls. I would think implicit feedback means preferences of
varying strengths and that could be seen in the input so I'm unsure about this
flag's meaning and use.
No idea what the confidence factor should be or how it is used.
Features? I suppose the number should be much less than the number of items but
there is a rule of thumb that applies to SVD so I wonder if there is also one
for ALS-WR?
Iterations seems straightforward since the greater the number the better the
results. I just need to see where the improvement is too small to warrant the
time spent.
The only parameter I wonder about for recommendfactorized is the maxRating? I
assume it is just a scaling factor so all ratings are between 0 and maxRating?
It doesn't do something unexpected like return anything > maxRating as
maxRating? In my case I have prefs 0-1 so maxRating is 1? I imagine that the
math might sometimes produce a rating higher than the max pref so this is to
clean up the returned ratings range?
