ok, thanks!
On Tue, Jan 22, 2013 at 8:59 PM, Sean Owen wrote:
> That's a question of using item-item similarity. For that you need to
> use something based on an ItemSimilarity, which is not user-based but
> instead the item-based implementation. Or you can just use
> ItemSimilarity directly to
That's a question of using item-item similarity. For that you need to
use something based on an ItemSimilarity, which is not user-based but
instead the item-based implementation. Or you can just use
ItemSimilarity directly to iterate over the possibilities and find
most similar, but, the recommende
Oh, I forgot one thing: Is it just as simple using the User-based
recommendation to find similar products, or is this only possible using
item-based recommendations? So basically if a given user rated a certain
product with x stars, to figure out what item is most like the one he has
just rated, bu
That's what i though. I just wanted to make sure!
Thanks so much for the quick reply!
HK
On Tue, Jan 22, 2013 at 7:40 PM, Sean Owen wrote:
> Yes that's right. Look as UserBasedRecommender.mostSimilarUserIDs(),
> and Recommender.estimatePreference(). These do what you are interested
> in, and
Yes that's right. Look as UserBasedRecommender.mostSimilarUserIDs(),
and Recommender.estimatePreference(). These do what you are interested
in, and yes they are easy since they are just steps in the
recommendation process anyway.
On Tue, Jan 22, 2013 at 6:38 PM, Henning Kuich wrote:
> Dear All,
>
Dear All,
I am wondering if I understand the User-based recommendation algorithm
correctly.
I need to be able to answer the following questions, given users and
ratings:
1) Which users are "closest" to a given user
and
2) given a user and a product, predict the preference for the product
apart
Yes any metric that concerns estimated value vs real value can't be
used since all values are 1. Yes, when you use the non-boolean version
with boolean data you always get 1. When you use the boolean version
with boolean data you will get nonsense since the output of this
recommender is not an esti
I got 0 when I used GenericUserBasedRecommender in code 2 but when
using GenericBooleanPrefUserBasedRecommender score was not 0 . I
repeat the test with different data and again I got some results.
Moreover , when I use
DataModel model = new FileDataModel(new File("ua.base"));
in code 2, the M
That sounds reversed. Are you sure? without pref values, you should
get 0. With values, you almost certainly won't get 0 RMSE. RMSE can't
be used with boolean data.
Code #4 needs to use the boolean user-based recommender or else you
will get "1" for all estimates and results are randomly ordered t
Thanks Sean.
- When I used GenericUserBasedRecommender in code 2 I got 0 , but when
using GenericBooleanPrefUserBasedRecommender both MAE and RMSE in case
2 gave me scores, so only RMSE is not useful or also MAE ?
- If I want to compare between recommenders that use preferences and
those that don
This is a standard problem in dense linear algebra.
The most established package to solve this problem is LAPACK.
There are newer packages, but this is a good reference point.
You first factor the matrix, DGETRF for a general matrix,
DSYTRF for a symmetric matrix, DPOTRF for a symmetric positive de
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