Thanks, that was exactly what I needed. I'll look into writing the necessary patches once I have Mahout up and running. cheers Klokie
On Fri, Sep 9, 2011 at 00:13, Daniel Xiaodan Zhou <[email protected]> wrote: > Thanks Sean. I think recommendedBecause() was the API I saw when > developing the Recommender API for Drupal, although I did remember > somewhere in the API documentation used the term "explain". > > @Klokie: The way Recommender API works is to pre-compute everything > and save the results to the Drupal database. If you like, you can work > on a patch to RecAPI that implements this. Of course, you need to > implement UserBasedRecommender.recommendedBecause() first in Mahout. I > can work on it too, but it won't be soon. > > > On Thu, Sep 8, 2011 at 4:29 PM, Sean Owen <[email protected]> wrote: >> I think he or she is just referring to the method >> ItemBasedRecommender.recommendedBecause(). This is as close to an "explain" >> operation as there is in the API. >> >> In reality recommendations are a function of all data. In practice, what you >> are asking for is the items most similar to well-liked items. >> Recommendations are a function of more than this, but you could say these >> are among the most influential reasons. >> >> Really you want something like UserBasedRecommender.recommendedBecause() >> since you're dealing in similar users, but that doesn't exist for no really >> good reason. You could implement this and make a patch, just by imitating >> the existing recommendedBecause() method. >> >> Sean >> >> On Thu, Sep 8, 2011 at 5:10 PM, Klokie Grossfeld <[email protected]> wrote: >> >>> Hi, I've just started working with the Recommender API for Drupal, >>> which integrates with Mahout. I'm reading up on Mahout, but I haven't >>> figured out how to determine which content has been used to compute a >>> given positive recommendation, i.e. how to obtain which nodes were >>> used to compute an index of similarity. For example, I would like to >>> display to the end user some text on a page they rated highly, like >>> "You may also like these other nodes, since two other people [with >>> similar affinities] also rated them highly". >>> >>> The developer of the Recommender API modules pointed me toward the >>> Mahout "explain" process, but I can't seem to find any information on >>> this. Could someone please point me in the right direction? >>> >>> thanks >>> Klokie >>> >> > -- // Daniel Marc « Klokie » Grossfeld // http://klokie.com/ // tel:+46707298075 // Skype:klokieg // AIM:klokie // MSN:klokie
