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From Sean Owen <sro...@gmail.com>
Subject Re: Recommend for anonymous users
Date Mon, 05 Jul 2010 15:52:22 GMT
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.
>

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