Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-99098372
@MLnick yes that's what I did...I have to convince users why use factor
vectors :-) For user->item recommendation, convincing is easy by showing the
ranking improvement through ALS
@srowen without coming up with a validation strategy, someone might propose
to run a different algorithm (KMeans on raw feature space followed by
(item->cluster) join (cluster->items)) and claims his item->item results are
better...how do we know whether ALS based flow is producing better result or
KMeans based flow ? NNALS can be thought of soft-kmeans as well and so these
flows are very similar.
I am focused on implicit feedback here because then only we can run either
KMeans or Similarity on raw feature space...With explicit feedback, I agree
that cosine similarity is not valid in original feature space. But in most
practical datasets, we are dealing with implicit feedback.
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