Github user gaborhermann commented on the issue:
https://github.com/apache/flink/pull/2542
Thanks @jfeher for the measurements! :)
@thvasilo The filtering referred to having distinct (user,artist) pairs.
It's only because the input of the iALS is a sparse matrix, and it would not
make much sense to have more than one values for the same element of the
matrix. E.g. to aggregate multiple listenings for the same (user,artist) pair,
one could count them, and use the count as the implicit rating. We simply used
the value 1.0 for every user-artist pair, but the algorithm works with any
(positive) values, not only binary interactions.
We've only measured Flink against itself, as the main ALS algorithm is
already in Flink. It would be interesting to measure against Spark and other
solutions, but that might not reflect the performance of our iALS extension,
but rather the performance of ALS itself. That seems to be another issue for
me. Do I see this right?
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