I have put my jar file in the lib directory,but I think the class still can't be imported.
On Fri, Jul 9, 2010 at 1:34 AM, Sean Owen <[email protected]> wrote: > You need to put the .jar file with your own code in the lib/ directory > under taste-web. Then the build script will package it into the web > app it builds. > > On Thu, Jul 8, 2010 at 4:37 PM, samsam <[email protected]> wrote: > > I implemented a recommender named AnonymousRecommender for anonymous > users, > > and in AnonymousRecommender I write a method like this to make > > recommendations. > > #----------- > > > > public synchronized List<RecommendedItem> recommend(PreferenceArray > > anonymousUserPrefs, int howMany) throws TasteException { > > > > plusAnonymousModel.setTempPrefs(anonymousUserPrefs); > > > > List<RecommendedItem> recommendations = > > > > recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, howMany, null); > > > > plusAnonymousModel.setTempPrefs(null); > > > > return recommendations; > > > > } > > > > #-------------- > > > > And in servlet I will use this recommender to process request,but I can't > > import the AnonymousRecommender class to invoke the recommend method I > > write. > > > > When mvn package, I got > > > /Users/samsam/Lab/mahout-0.3/taste-web/src/main/java/org/apache/mahout/cf/taste/web/AnonymousRecommenderServlet.java:[36,38] > > package net.gamestreamer.recommendation does not exist > > > > Who knnow how to import the AnonymousRecommender class? > > > > Best Regards. > > > > On Thu, Jul 8, 2010 at 12:48 AM, samsam <[email protected]> wrote: > >> > >> 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. > > > > > > > > -- > > I'm samsam. > > > -- I'm samsam.
