Hi Em,
If I understood well what you're asking, you could implement a new
CandidateItemStrategy class. If you see that interface, there's this
method getCandidateItems(long userID, DataModel dataModel) that has all
parameters you need in order to filter out items that belong to the
unwanted category.
This class is actually used inside an item-based recommender.
On 07/02/2011 03:53 PM, Em wrote:
Hi Steven,
That would be the alternative. Create different data-models per
category, yes.
Does this affect the quality of your recommendations in comparison to a
data-model that contains also not-relevant data for the current
category/situation/social-graph but the unwanted recommendations are
filtered out by a Rescorer?
Regards,
Em
Am 02.07.2011 15:22, schrieb Steven Bourke:
Assuming you have the technical resources, one approach could involve just
containing different 'conditions' into different data models.
For instance I have one setup that only has users from someones social
graph, and another that includes all my users. When generating
recommendations I just point it to whichever datasource is required.
On Fri, Jul 1, 2011 at 7:25 PM, Sean Owen<[email protected]> wrote:
From what you describe so far, you do not need any new code. A
Rescorer does what you want, I believe. If not, maybe you can explain
more about what it's not doing that you want it to do. A Rescorer to
exclude items is probably always a good idea as it saves computation.
On Fri, Jul 1, 2011 at 7:03 PM, Em<[email protected]> wrote:
Hi Sean,
I am not very familiar with the code itself, however I have no problem
with digging into it.
I guess the CandidateItemStrategy and the Rescorer are usable for all
kinds of recommendations: user-user, user-item, item-item etc. and so I
can create a generic (or general) implementation for the problem?
Could you explain more of the tradeoffs for both
implementation-possibilities, please?
Regards,
Em
Am 01.07.2011 19:01, schrieb Sean Owen:
The short answer is that you'd have to modify the code to inject this
kind of logic -- though you might get away with just using a custom
CandidateItemStrategy in an item-based recommender.
A Rescorer will cause it to not bother computing estimated values for
unwanted items though, so I think it already does what you intend.
On Fri, Jul 1, 2011 at 5:56 PM, Em<[email protected]>
wrote:
Hello list,
is it possible to filter out some items/users from the
recommendation-process?
In some cases one does not want to include information from some
sources in
special situations.
As an example you can imagine an onlineshop. If you click on the
category
"women" it would be the best to only show recommendations for this
main-category rather than also showing some stuff for men.
A Rescorer could be a solution to filter out those unwanted results
*after*
the big part is done (am I correct?), however I do not want to spend
ressources on computing probabilities for items that are definitly
unwanted
for the resultset.
What I want is something like a
SELECT col1, col2, col3 FROM myData WHERE category = "women" OR
category =
"subcategoryOfWomen"
and than do the computation on top of this dataset.
Is this possible with Mahout?
Regards,
Em
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