Hi,
mainly for the records:
I've now mapped my items onto what in Mahout is called "User", and
mapped the categories onto Mahout "Items", instead of mapping my items
onto "Item" and the categories onto "User".
I changed the plan because that way, it was easier to create the
GenericBooleanPrefDataModel from my input. I actually think that it fits
better that way - what's your opinion?
The input to the data model looks a bit like this (I've shortened it for
the sake of readability):
[id=15901,title=Infamous] CAT1={3=Drama}
[id=15888,title=Millions] CAT1={3=Drama, 4=Crime, 8=Thriller}
[id=16421,title=The Departed] CAT1={3=Drama, 8=Thriller}
NOTE that the data from the second category system is MISSING!
(I have not yet all data accumulated, but while waiting for it I am
preparing the code to process the similarities.) It would come as an
additional list per item:
CAT2={<id>=<value> ...}
Where id is in a distinctly different range from the ids used for CAT1.
I am using the code from Grant Ingersoll's article:
// prefs is:
// FastByIDMap with id:=itemId,
// FastIDSet := list of CAT1 (and CAT2) ids
DataModel dataModel = new GenericBooleanPrefDataModel(prefs);
ItemSimilarity itemSimilarity = new LogLikelihoodSimilarity(dataModel);
ItemBasedRecommender recommender =
new GenericItemBasedRecommender(dataModel, itemSimilarity);
//Get the recommendations for the Item
// loop over all items
for (items in CAT1) {
List<RecommendedItem> simItems =
recommender.mostSimilarItems(id, numRecs);
// filter out CAT1, keep only CAT2
}
I've run the code but as CAT2 is missing, currently, I am not filtering
the results. It seems fine, from what I can tell.
Thanks again for your help!
Chantal
