thanks very much! On Thu, Jul 8, 2010 at 12:46 AM, Sean Owen <[email protected]> wrote:
> That's a bit of example code for the book. It is in the source code > made available with the MEAP book. It should be downloadable -- if > it's not apparent where it's available I'll ask Manning where it is. > > I can send it to you -- see attached. You should get it though the > mailing list won't I believe. But you should find all the source since > there are more classes than just this. > > Sean > > On Wed, Jul 7, 2010 at 5:42 PM, samsam <[email protected]> wrote: > > 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. > > > -- I'm samsam.
