Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/3536#issuecomment-65393291
I wasn't necessarily suggesting changing the similarity metric although I
ended up using cosine too. Note you can skip normalizing by the target
item's norm.
I suppose my point is that the recommendation computation does not use a
dot product because it is performing a similarity computation. Those
vectors are not even in the same space. So I wouldn't reuse that logic on
the grounds that it is reusing a similarity computation.
On Dec 3, 2014 5:03 AM, "Steven" <[email protected]> wrote:
> Re: Explaining similarity metric [image: :+1:] I'll do that.
>
> Re: Cosine - no biggie to add. I used dot product because 1) Taking the
> logic that CF is finding "similar" items based on the latent space for a
> user when recommending products and 2) Using dot product would reduce the
> new code added to MatrixFactorizationModel ( I don't want to create
clutter
> :)) So [image: :+1:] will change to cosine
>
> Re: Popularity, I'll look into that as well then.
>
> â
> Reply to this email directly or view it on GitHub
> <https://github.com/apache/spark/pull/3536#issuecomment-65391077>.
>
---
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]
