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From Sean Owen <sro...@gmail.com>
Subject Re: Recommend for anonymous users
Date Wed, 07 Jul 2010 16:46:17 GMT
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.
>

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