Alessandro,
which version of Mahout are you using? I had a look at the current
implementation of GenericBooleanPrefUserBasedRecommender and its
doEstimatePreference method returns the sum of similarities of users
that have also interacted with the item. So that should be different
from either 0 or 1.
--sebastian
On 05/03/2014 05:00 PM, Alessandro Suglia wrote:
Sorry Sebastian, maybe you haven't the possibility to read the post on
SO, so I'll report the code here.
I've already used the GenericBooleanPrefUserBasedRecommender in order to
generate the recommendation and the results are the same.
| DataModel trainModel= new FileDataModel(new
File(String.valueOf(Main.class.getResource("/binarized/u1.base").getFile())));
DataModel testModel= new FileDataModel(new
File(String.valueOf(Main.class.getResource("/binarized/u1.test").getFile())));
UserSimilarity similarity= new
TanimotoCoefficientSimilarity(trainModel);
UserNeighborhood neighborhood= new NearestNUserNeighborhood(35,
similarity, trainModel);
GenericBooleanPrefUserBasedRecommender userBased= new
GenericBooleanPrefUserBasedRecommender(trainModel, neighborhood,
similarity);
long firstUser= testModel.getUserIDs().nextLong(); // get the
first user
// try to recommender items for the first user
for(LongPrimitiveIterator iterItem=
testModel.getItemIDsFromUser(firstUser).iterator();
iterItem.hasNext(); ) {
long currItem= iterItem.nextLong();
// estimates preference for the current item for the first user
System.out.println("Estimated preference for item" +
currItem+ " is" + userBased.estimatePreference(firstUser, currItem));
}
|
Can you explain to me where is the error in this code?
Thank you.
On 05/03/14 16:42, Sebastian Schelter wrote:
You should try the
org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefUserBasedRecommender
which has been built to handle such data.
Best,
Sebastian
On 05/03/2014 04:34 PM, Alessandro Suglia wrote:
I have described it in the SO's post:
"When I execute this code, the result is a list of 0.0 or 1.0 which are
not useful in the context of top-n recommendation in implicit feedback
context. Simply because I have to obtain, for each item, an estimated
rate which stays in the range [0, 1] in order to rank the list in
decreasing order and construct the top-n recommendation appropriately."
On 05/03/14 16:25, Sebastian Schelter wrote:
Hi Allessandro,
what result do you expect and what do you get? Can you give a concrete
example?
--sebastian
On 05/03/2014 12:11 PM, Alessandro Suglia wrote:
Good morning,
I've tried to create a recommender system using Mahout in an implicit
feedback situation. What I'm trying to do is explained exactlly in
this
post on stack overflow:
http://stackoverflow.com/questions/23077735/mahout-recommendation-in-implicit-feedback-situation.
<http://stackoverflow.com/questions/23077735/mahout-recommendation-in-implicit-feedback-situation>
As you can see, I'm having some problem with it simply because I
cannot
get the result that I expect (a value between 0 and 1) when I try to
predict a score for a specific item.
Someone here can help me, please?
Thank you in advance.
Alessandro Suglia
