I seen LibimsetiRecomender in book <mahout in action>,but i can't find it in mahout docs.What is it?
On Tue, Jul 6, 2010 at 12:07 AM, samsam <[email protected]> wrote: > I become more clear about that,thanks for your help very much. > > > On Mon, Jul 5, 2010 at 11:52 PM, Sean Owen <[email protected]> wrote: > >> Pre-compute the similarity based on what information? You mention that >> you don't want to use Pearson and mention item attributes. >> >> If you are trying to use domain-specific attributes of items, then >> it's up to you to write that logic. If you want to say books have a >> "0.5" similarity when they are within the same genre, and "0.9" when >> by the same author, you can just write that logic. That's not part of >> the framework. >> >> The hook into the framework comes when you implement ItemSimilarity >> with logic like that. Then just use that ItemSimilarity instead of one >> of the given implementations. That's all. >> >> On Mon, Jul 5, 2010 at 4:32 PM, samsam <[email protected]> wrote: >> > About the second question,I have not the similarity,I want to know is >> how to >> > pre-compute the item similarity. >> > >> > On Mon, Jul 5, 2010 at 11:20 PM, Sean Owen <[email protected]> wrote: >> > >> >> 1) Good question. One answer is to make these "anonymous" users real >> >> users in your data model, at least temporarily. That is, they need not >> >> be anonymous to the recommender, even if they're not yet a registered >> >> user as far as your site is concerned. >> >> >> >> There's a class called PlusAnonymousUserDataModel that helps you do >> >> this. It wraps a DataModel and lets you quickly add a temporary user, >> >> recommend, then un-add that user. It may be the easiest thing to try. >> >> >> >> (BTW the book Mahout in Action covers this in section 5.4, in the >> >> current MEAP draft.) >> >> >> >> 2) Not sure I fully understand. You already have some external, >> >> pre-computed notion of item similarity? then just feed that in to >> >> GenericItemSimilarity and use it from there. >> >> >> >> Sean >> >> >> >> On Mon, Jul 5, 2010 at 1:52 PM, samsam <[email protected]> wrote: >> >> > Hello,all >> >> > I want to build recommendation engine with apache mahout,I have read >> some >> >> > reading material,and I still have some questions. >> >> > >> >> > 1)How to recommend for anonymous users >> >> > I think recommendation engine should return recommendations given a >> item >> >> > id.For example,a anonymous user reviews some items, >> >> > and tell the recommendation what he reviews,and compute with the >> reviews >> >> > histories. >> >> > >> >> > 2)How to compute the items similarity dataset >> >> > Without use items similarity dataset,we can make ItemBasedRecommender >> >> > with PearsonCorrelationSimilarity,but >> >> > we need to make recommendations with extra attributes of items, >> >> > so we should use the items similarity dataset,how to build the >> dataset is >> >> > the key point. >> >> > -- >> >> > I'm samsam. >> >> > >> >> >> > >> > >> > >> > -- >> > I'm samsam. >> > >> > > > > -- > I'm samsam. > -- I'm samsam.
