Hi Tefik, Thanks for the response. I think what you says contradicts what Sebastian pointed out before. Also, if AllSimilarItemsCandidateItemsStrategy returns all items that have not been rated by the user, what would AllUnknownItemsCandidateItemsStrategy return?
On Wed, Mar 5, 2014 at 1:40 PM, Tevfik Aytekin <tevfik.ayte...@gmail.com>wrote: > 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. > >>>>>> > >>>>> > >>>> > >>> > >> >
