Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-65042436
I think it's essential to explain (even in internal comments, or this PR)
what the similarity metric is. It's just ranking by dot product, which makes it
something like cosine similarity. The differences are that it isn't in [-1,1],
and the result doesn't normalize away the length of the feature vectors. This
tends to favor popular items, or mean that somewhat less similar items may rank
higher because they're popular. I had traditionally viewed that as a negative,
and preferred the more standard cosine similarity, but it's certainly up for
debate.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
