Hi,
If one is using a matrix factorization based method, in order to generate a
top-N recommendation to a user, all the unknown ratings of that user needs
to be predicted (so that highest predicted N items can be recommended). If
we are talking about a site with millions of items this means that
Hi,
I setup a web service using Mahout. Everything worked fine. However, I
anticipate high traffic. That is, the system will produce quite a lot of
recommendations every second. I'm quite inexperienced in things like load
balancing. Any advice or pointers are greatly appreciated.
Thanks
distribution with an unknown parameter p_A. Likewise with B and p_B. The
two binomial distributions may or may not be independent.
The LLR is measuring the degree evidence against independence.
On Thu, May 1, 2014 at 12:50 AM, Mario Levitin mariolevi...@gmail.com
wrote:
Ted, I understand how
not
use the contingency table.
I hope my question is clear.
Thanks.
On Mon, Apr 28, 2014 at 2:41 AM, Ted Dunning ted.dunn...@gmail.com wrote:
Excellent. Look forward to hearing your reactions.
On Mon, Apr 28, 2014 at 1:14 AM, Mario Levitin mariolevi...@gmail.com
wrote:
Not yet, but I
, 2014 at 11:31 PM, Mario Levitin mariolevi...@gmail.com
wrote:
Hi Ted,
I have read the paper. I understand the Likelihood Ratio for Binomial
Distributions part.
However, I cannot make a connection with this part and the contingency
table.
In order to calculate Likelihood Ratio for two
Hi,
I've used LogLikelihood Similarity in user based nearest neighborhood
collaborative filtering and it has given good results (better than the
others).
I have read the blog post by Ted Dunning (
http://tdunning.blogspot.com.tr/2008/03/surprise-and-coincidence.html) also
looked at the
Not yet, but I will.
Thanks
On Mon, Apr 28, 2014 at 2:01 AM, Ted Dunning ted.dunn...@gmail.com wrote:
On Mon, Apr 28, 2014 at 12:30 AM, Mario Levitin mariolevi...@gmail.com
wrote:
I'm trying to give it a probabilistic interpretation in order to
understand
the logic behind. Any
whether the patch from https://issues.apache.org/
jira/browse/MAHOUT-1517 fixes your problem?
Best,
Sebastian
On 04/18/2014 11:03 PM, Mario Levitin wrote:
In my dataset ID's are strings so I use MemoryIDMigrator. This migrator
produces large longs.
I'm not doing any translation.
I could
ted.dunn...@gmail.com wrote:
Are you translating the ID's down into a range that will fit into int's?
On Thu, Apr 17, 2014 at 3:02 PM, Mario Levitin mariolevi...@gmail.com
wrote:
Hi,
I'm trying to run the ALS algorithm. However, I get the following error:
Exception in thread pool-1
Hi,
I'm trying to run the ALS algorithm. However, I get the following error:
Exception in thread pool-1-thread-3
org.apache.mahout.math.IndexException: Index -691877539 is outside
allowable range of [0,2147483647)
at org.apache.mahout.math.AbstractVector.set(AbstractVector.java:395)
at
releases come out you won't have to manage a fork.
On Mar 2, 2014, at 12:38 PM, Mario Levitin mariolevi...@gmail.com
wrote:
Juan, I don't understand your solution, if there are no ratings how can
you
blend the recommendations from the system and the user's already read
news.
Anyway, I
you be willing to set up a jira issue and create a patch for this?
--sebastian
On 03/04/2014 11:58 PM, Mario Levitin wrote:
I think we should introduce a new parameter for the recommend() method in
the Recommender interface that tells whether already known items should
be
recommended
Juan, I don't understand your solution, if there are no ratings how can you
blend the recommendations from the system and the user's already read news.
Anyway, I think, as Pat does, the best way is to remove the mentioned line.
It should be the responsibility of the business logic to remove
Hi,
Mahout do not recommend items which are already consumed by the user.
For example,
In the getAllOtherItems method of GenericUserBasedRecommender class there
is the following line
possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(theUserID));
which removes user's items from the
Hi,
In my dataset there are no ratings. There is information only about which
users rented which movies.
I think SVD is not suitable in this case. What about SVD++? I heard that it
can be used with this kind of data. Are there any other alternatives (other
than neighborhood based ones)?
Thanks
ratio as similarity measure or an SVDRecommender with an ALSWRFactorizer
with usesImplicitFeedback = true
--sebastian
On 01/09/2014 01:57 PM, Mario Levitin wrote:
Hi,
In my dataset there are no ratings. There is information only about which
users rented which movies.
I think SVD
/ManuelB/facebook-recommender-demo
If you need more support I would recommend to contact one of the
supporting companies like MapF, Cloudera or Apaxo (my own company).
/Manuel
On 02.12.2013, at 22:49, Mario Levitin wrote:
Hi,
I'm trying to build a web service for Mahout.
If I use
Hi all,
In some recommender applications the system might recommend already
consumed items. For example, a hotel recommendation site might recommend
hotel A to a user who already stayed at hotel A before.
In order to recommend already consumed items we have to rank all of the
items (consumed and
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