Hi Juan, If I remember correctly, AllSimilarItemsCandidateItemsStrategy
returns all items that have not been rated by the user and the similarity metric returns a non-NaN similarity value that is with at least one of the items preferred by the user. Tevfik On Wed, Mar 5, 2014 at 2:30 PM, Sebastian Schelter <s...@apache.org> wrote: > On 03/05/2014 01:23 PM, Juan José Ramos wrote: >> >> Thanks for the reply, Sebastian. >> >> I am not sure if that should be implemented in the Abstract base class >> though because for >> instance PreferredItemsNeighborhoodCandidateItemsStrategy, by definition, >> it returns the item not rated by the user and rated by somebody else. > > > Good point. So we seem to need special implementations. > > >> >> Back to my last post, I have been playing around with >> AllSimilarItemsCandidateItemsStrategy >> and AllUnknownItemsCandidateItemsStrategy, and although they both do what >> I >> wanted (recommend items not previously rated by any user), I honestly >> can't >> tell the difference between the two strategies. In my tests the output was >> always the same. If the eventual output of the recommender will not >> include >> items already rated by the user as pointed out here ( >> >> http://mail-archives.apache.org/mod_mbox/mahout-user/201403.mbox/%3CCABHkCkuv35dbwF%2B9sK88FR3hg7MAcdv0MP10v-5QWEvwmNdY%2BA%40mail.gmail.com%3E), >> AllSimilarItemsCandidateItemsStrategy should be equivalent to >> AllUnkownItemsCandidateItemsStrategy, shouldn't it? > > > AllSimilarItems returns all items that are similar to any item that the user > already knows. AllUnknownItems simply returns all items that the user has > not interacted with yet. > > These are two different things, although they might overlap in some > scenarios. > > Best, > Sebastian > > > >> >> Thanks. >> >> On Wed, Mar 5, 2014 at 10:23 AM, Sebastian Schelter <s...@apache.org> >> wrote: >>> >>> >>> Hi Juan, >>> >>> that is a good catch. CandidateItemsStrategy is the right place to >> >> implement this. Maybe we should simply extend its interface to add a >> parameter that says whether to keep or remove the current users items? >>> >>> >>> We could even do this in the abstract base class then. >>> >>> --sebastian >>> >>> >>> On 03/05/2014 10:42 AM, Juan José Ramos wrote: >>>> >>>> >>>> In case somebody runs into the same situation, the key seems to be in >>>> the >>>> CandidateItemStrategy being passed to the constructor >>>> of GenericItemBasedRecommender. Looking into the code, if no >>>> CandidateItemStrategy is specified in the >>>> constructor, PreferredItemsNeighborhoodCandidateItemsStrategy is used >>>> and >>>> as the documentation says, the doGetCandidateItems method: "returns all >>>> items that have not been rated by the user and that were preferred by >>>> another user that has preferred at least one item that the current user >> >> has >>>> >>>> preferred too". >>>> >>>> So, a different CandidateItemStrategy needs to be passed. For this >> >> problem, >>>> >>>> it seems to me that AllSimilarItemsCandidateItemsStrategy, >>>> AllUnknownItemsCandidateItemsStrategy are good candidates. Does anybody >>>> know where to find some documentation about the different >>>> CandidateItemStrategy? Based on the name I would say that: >>>> 1) AllSimilarItemsCandidateItemsStrategy returns all similar items >>>> regardless of whether they have been already rated by someone or not. >>>> 2) AllUnknownItemsCandidateItemsStrategy returns all similar items that >>>> have not been rated by anyone yet. >>>> >>>> Does anybody know if it works like that? >>>> Thanks. >>>> >>>> >>>> On Tue, Mar 4, 2014 at 9:16 AM, Juan José Ramos <jjar...@gmail.com> >> >> wrote: >>>> >>>> >>>>> First thing is thatI know this requirement would not make sense in a CF >>>>> Recommender. In my case, I am trying to use Mahout to create something >>>>> closer to a Content-Based Recommender. >>>>> >>>>> In particular, I am pre-computing a similarity matrix between all the >>>>> documents (items) of my catalogue and using that matrix as the >>>>> ItemSimilarity for my Item-Based Recommender. >>>>> >>>>> So, when a user rates a document, how could I make the recommender >> >> outputs >>>>> >>>>> similar documents to that ones the user has already rated even if no >> >> other >>>>> >>>>> user in the system has rated them yet? Is that even possible in the >> >> first >>>>> >>>>> place? >>>>> >>>>> Thanks a lot. >>>>> >>>> >>> >> >
