Hello friends,

I am considering the use of Cluster-based recommender for a problem that I
am solving. However, what I am trying to do the mirror or how cluster-based
is doing. Specifically, cluster-based clusters users to groups then
recommends items to each cluster. Would it be possible for me to cluster
items as groups then recommend users to these groups instead? Does this
sound acceptable using this method?

Please let me know.

Thanks,
-Ahmed

Sure -- to do this, you simply flip your items and users. Feed item
IDs as user IDs and vice versa. Then you have a system that recommends
users to items, really. And you can use clustering if you like, to do
that. In fact you can use any algorithm.

Sean

On Mon, Mar 12, 2012 at 1:56 PM, Ahmed Abdeen Hamed
<[EMAIL PROTECTED]> wrote:
> Hello friends,
>
> I am considering the use of Cluster-based recommender for a problem that I
> am solving. However, what I am trying to do the mirror or how cluster-based
> is doing. Specifically, cluster-based clusters users to groups then
> recommends items to each cluster. Would it be possible for me to cluster
> items as groups then recommend users to these groups instead? Does this
> sound acceptable using this method?
>
> Please let me know.
>
> Thanks,
> -Ahmed

This is really great. Thanks so much!
-Ahmed

On Mon, Mar 12, 2012 at 12:13 PM, Sean Owen <[EMAIL PROTECTED]> wrote:

> Sure -- to do this, you simply flip your items and users. Feed item
> IDs as user IDs and vice versa. Then you have a system that recommends
> users to items, really. And you can use clustering if you like, to do
> that. In fact you can use any algorithm.
>
> Sean
>
>

Be aware that cluster based recommenders almost never perform as well as
user/item based recommenders.

On Mon, Mar 12, 2012 at 10:03 AM, Ahmed Abdeen Hamed <
[EMAIL PROTECTED]> wrote:

> This is really great. Thanks so much!
> -Ahmed
>
> On Mon, Mar 12, 2012 at 12:13 PM, Sean Owen <[EMAIL PROTECTED]> wrote:
>
> > Sure -- to do this, you simply flip your items and users. Feed item
> > IDs as user IDs and vice versa. Then you have a system that recommends
> > users to items, really. And you can use clustering if you like, to do
> > that. In fact you can use any algorithm.
> >
> > Sean
> >
> >
>

Thanks for the heads up. I will keep that in mind!

-Ahmed

On Mon, Mar 12, 2012 at 1:49 PM, Ted Dunning <[EMAIL PROTECTED]> wrote:

> Be aware that cluster based recommenders almost never perform as well as
> user/item based recommenders.
>
>

I have a question about the TreeClusteringRecommender:

Is there a way that you can estimate the number of clusters rather than
specifying how many you want? I thought of using the K-means clustering
algorithm to do that for me, but it sounds a bit redundant. Is there a more
elegant way?

Thanks,

-Ahmed

On Mon, Mar 12, 2012 at 1:49 PM, Ted Dunning <[EMAIL PROTECTED]> wrote:

> Be aware that cluster based recommenders almost never perform as well as
> user/item based recommenders.

You can set a threshold rather than a count -- that's about as much as
that bit of code does in this regard.

On Mon, Mar 12, 2012 at 10:18 PM, Ahmed Abdeen Hamed
<[EMAIL PROTECTED]> wrote:
> I have a question about the TreeClusteringRecommender:
>
> Is there a way that you can estimate the number of clusters rather than
> specifying how many you want? I thought of using the K-means clustering
> algorithm to do that for me, but it sounds a bit redundant. Is there a more
> elegant way?

This makes perfect sense!

Thanks again,

-Ahmed

On Mon, Mar 12, 2012 at 6:20 PM, Sean Owen <[EMAIL PROTECTED]> wrote:

> You can set a threshold rather than a count -- that's about as much as
> that bit of code does in this regard.
>
>