Glad to help.
You can help us by reporting your results when you get them.
We look forward to that!
On Tue, Mar 10, 2015 at 4:22 AM, Efi Koulouri wrote:
> Things got clearier with your help!
>
> Thank you very much
>
> On 9 March 2015 at 01:50, Ted Dunning wrote:
>
> > Efi,
> >
> > Only you
Things got clearier with your help!
Thank you very much
On 9 March 2015 at 01:50, Ted Dunning wrote:
> Efi,
>
> Only you can really tell which is best for your efforts. All the rest is
> our own partially informed opinions.
>
> Pre-filtering can often be accomplished in the search context by c
Efi,
Only you can really tell which is best for your efforts. All the rest is
our own partially informed opinions.
Pre-filtering can often be accomplished in the search context by creating
more than one indicator field and using different combinations of
indicators for different tasks. For inst
Either architecture will work. Even if you want to pre-filter the data. The
search engine can post-filter in the query. The pre-filter is to create a
separate model for each day of the week, right? So works with any one.
If you are relying on the evaluator implemented in Mahout then use the old
Thanks for your help!
Actually, I want to build a recommender for experimental purposes following
the pre-filtering and post-filtering approaches that I described. I have
already two datasets and I want to show the benefits of using a
"context-aware" recommender. So,the recommender is going to wor
The by far easiest way to build a recommender (especially for production)
is to use the search engine approach (what Pat was recommending).
Post filtering can be done using the search engine far more easily than
using Java classes.
On Sat, Mar 7, 2015 at 8:44 AM, Pat Ferrel wrote:
> Ooops a s
Ooops a several typos corrected below
On Mar 7, 2015, at 7:05 AM, Pat Ferrel wrote:
The new cooccurrence recommender can use context as part of the user history,
or as a method to bias or filter results. In any case you want to record all
actions. Filtering results is easy and tossing all data
The new cooccurrence recommender can use context as part of the user history,
or as a method to bias or filter results. In any case you want to record all
actions. Filtering results is easy and tossing all data but for one day of the
week will reduce your cooccurrences and the quality of your da
Thanks for your reply!
Actually, the context in pre-filtering serves as a query for selecting
relevant data. An example of a contextual data filter for a movie
recommender system would be: if a person wants to see a movie on Saturday,
only the Saturday rating data is used to recommend movies.
So,w
The new Spark based recommender can easily handle context in many forms. See
the top references section here
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
It does not use the IDRescorer approach at all so perhaps you should describe
what you want to use as context.
I
Hi all,
I am trying to implement an context-aware recommender in Mahout. As I
haven't use the library before I haven't a lot experience. So, I would
really appreciate your response!
What I want to do is to implement the two context- aware approaches that
have been proposed, pre-filtering and post
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