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From samsam <yangua...@gmail.com>
Subject Re: Recommend for anonymous users
Date Fri, 09 Jul 2010 01:50:42 GMT
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 <srowen@gmail.com> 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 <yanguango@gmail.com> 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 <yanguango@gmail.com> wrote:
> >>
> >> thanks very much!
> >>
> >> On Thu, Jul 8, 2010 at 12:46 AM, Sean Owen <srowen@gmail.com> 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 <yanguango@gmail.com> 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 <yanguango@gmail.com>
wrote:
> >>> >
> >>> >> I become more clear about that,thanks for your help very much.
> >>> >>
> >>> >>
> >>> >> On Mon, Jul 5, 2010 at 11:52 PM, Sean Owen <srowen@gmail.com>
> 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 <yanguango@gmail.com>
> 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 <srowen@gmail.com>
> >>> >>> > 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 <yanguango@gmail.com>
> >>> >>> >> 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.

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