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From Sebastian Schelter <...@apache.org>
Subject Re: get similar items
Date Mon, 27 Sep 2010 07:23:03 GMT
I see the problem now.

The estimators (MostSimilarEstimator or MultiMostSimilarEstimator) used
to fetch the most similar items only use the ItemSimilarity
implementation and are therefore not dependent on having preferences.

But the initial set of "candidate" items that will be given to the
estimators in GenericItemBasedRecommender.doMostSimilarItems() is
fetched from the DataModel (it consists of all items preferred by a user
that also preferred the current item), which makes the computation
dependent on preference data being available.

The short term solution would be to subclass GenericItemBasedRecommender
and create your own customized way of candidate item fetching. The long
term solution should be to enable users to customize this like we
already did for recommendation with the CandidateItemsStrategy).

--sebastian

Am 27.09.2010 09:08, schrieb Sam Yang:
> I still get no results when GenericItemBasedRecommender.mostSimilarItems(),and
> my ItemSimilarity works well.
> Does GenericItemBasedRecommender.mostSimilarItems() works without preference
> data?
> Tim said:
> "As Sebastian pointed out, you could start out by making recommendations
> strictly on content-based similarities (such as the output from a More Like
> This query in Lucene) and then introduce recommendations based on
> collaborative filtering as your user-item interaction data grows."
> Do I get the similar items with other tool.like Lucene,can Mahout do it
> without preference data at start?
>
> On Mon, Sep 27, 2010 at 2:59 PM, Sean Owen <srowen@gmail.com> wrote:
>
>   
>> OK, so that issue was fixed. What is your current issue then?
>>
>> On Mon, Sep 27, 2010 at 3:37 AM, Sam Yang <yanguango@gmail.com> wrote:
>>     
>>> And it's like this issue:
>>>       
>> https://issues.apache.org/jira/browse/MAHOUT-367
>>
>>     
>
>
>   


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