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.
