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From Marko Ciric <ciric.ma...@gmail.com>
Subject Re: Advice request
Date Mon, 08 Aug 2011 16:58:26 GMT
You could also introduce clustering and build clusters from pages that have
a lot of similar words. If your pages data doesn't change too often, you
could select most similar pages from within a cluster and recommend it to a
user..
On Aug 8, 2011 6:08 PM, "Marko Ciric" <ciric.marko@gmail.com> wrote:
> You might want to use TanimotoCoefficientSimilarity if your data set isn't
> large.
> On Jul 27, 2011 10:51 AM, "Sean Owen" <srowen@gmail.com> wrote:
>> Sounds good. In that case, the surprise-n-coincidence counterpart you are
>> probably looking for it LogLikelihoodSimilarity, which implements
>> ItemSimilarity. Use it with a GenericBooleanPrefItemBasedRecommender and
> you
>> can recommend new words to use.
>>
>> On Wed, Jul 27, 2011 at 9:01 AM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
>>
>>> Actually, I think that recommending words to people and then doing the
>>> search may add some mileage.
>>>
>>> On Wed, Jul 27, 2011 at 12:38 AM, Sean Owen <srowen@gmail.com> wrote:
>>>
>>> > It's just a search problem as Ted says -- minus
>>> > even the recommendation phase.
>>> >
>>> > Is that all you want? then try Lucene, probably.
>>> >
>>>

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