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. >
