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From Sean Owen <>
Subject Re: Custom Item Similarity :datamodel not sure
Date Thu, 27 Sep 2012 17:53:35 GMT
The input to the recommender remains the same -- user,item,rating.
Your similarities are used as weights in a weighted average to make
recommendations. This is unrelated -- or rather, not necessarily
related at all -- to whatever custom similarity metric you create.
Your similarities do not need to be precomputed. You could, but it's
not necessary.

On Thu, Sep 27, 2012 at 6:48 PM, Abhishek Roy <> wrote:
> Sean Owen <srowen <at>> writes:
>> File for FileDataModel? This does not change. But that input does not
>> consist of item item pairs. Are you talking about something else?
>> On Sep 27, 2012 5:10 PM, "Abhishek Roy" <abhishekroy8 <at>>
>> > Hi Sean,
>> > For using a custom ItemSimilarity what should my data model file(item id1,
>> > item
>> > id2) include ?
>> >
>> > Please advise.
>> >
>> > Thanks,
>> > Roy
>> >
>> >
> Thanks for the quick response Sean.
> My end goal (short term) is to show "related / similar" items for my site when
> the user(any user, including unregistered user) is looking at a particular item.
> Basically I am looking at (rather created) a custom ItemSimilarity using domain
> specific attributes that computes a similarity score between a pair of items. I
> am using a GenericItemBasedRecommender and then calling n mostSimilarItems() to
> get my recommendations. The problem is, and I didn't see anything on that in the
> book as well as the forum, that I am not sure about the data model to feed to
> the GenericItemBasedRecommender. I did a brute force, computed nC2(combinations)
> of {item id, item id} pairs and fed that as the data model. Works, but
> definitely not scalable and sensible. What data model does this kind of a system
> need ? I am not having preference data(very little), and since this is content
> based recommendation, am puzzled about the data to be encapsulated by the
> datamodel. I hope I am clear..
> Please suggest...

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