X

At each iteration calculate MAE for all of the items (pick next item randomly)

Profiling

X

May add enhancement that uses only top n neighbors.  Might implement by using group by and count (neighbor that has the most items in common should be seleted).

Could select best on two criterions:

1) the users have rated the most items in common

[optional]

2) the user have rated the most test items (if use this criteria for all of the methods, it is a fair one)

X -

unexpected results for Random (MAE does not decrease)

Double check that correlation is calculated correctly

double check MAE calculation etc.