Sorry there was a typo in the previous paragraph. 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 with at least one of the items preferred by the user. On Wed, Mar 5, 2014 at 3:38 PM, Tevfik Aytekin <tevfik.ayte...@gmail.com> wrote: > 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. >>>>>> >>>>> >>>> >>> >>
