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From Julian Ortega <jorte...@gmail.com>
Subject Re: Correct way for merging different data sources
Date Fri, 21 Sep 2012 08:52:14 GMT
One could argue that the rating is really just an indication of how strong
the preference from the user is to the item, so the stronger the
preference, the higher the rating value should be.

For instance, you could say that a purchase is the strongest indication of
preference and that it will have a value of 10. Then you could say that
adding to the wishlist is your second most strong indicator of preference
and have that with a value of 5. The view would be the less strong
indication and you can have that with a value of say 1. I wouldn't know how
to go about representing the likes, since they already have their own
scale, but if you just had a list of people who liked certain items (kind
of like Facebook, you either liked or you didn't do anything), you could
say that this indicator of preference could have a value of 3.

Those are just example values, you would need to determine how much
stronger you want the different indicators of preference to be in relation
to one another.

Cheers

On Fri, Sep 21, 2012 at 10:19 AM, Davide Pozza <davide.pozza@gmail.com>wrote:

> Dear all
> this is probably a newbie question...
>
> From a tipical ecommerce scenario I can obtain the following kind of data
> which can be used for recommending products:
>
> 1) Users bought items - without ratings (csv format: USER_ID,ITEM_ID)
> 2) User viewed items (csv format: USER_ID,ITEM_ID, RATING) where RATING
> could represent the number of views
> 3) User likes (csv format: USER_ID,ITEM_ID, RATING) where RATING is a
> number form 1 to 5
> 4) User wishlist - without ratings (csv format: USER_ID,ITEM_ID)
>
> My question is: which is the right way to build my recommendations by using
> all these available infos in order to show a generic section "Other items
> you could be interested on"?
>
> I suppose I should create different recommenders for each kind of data and
> then merge their results (the resulting score for a single recommended item
> will be the sum of the score assigned by each single recommender). Is this
> the right way?
>
> Thanks!
>
> --
> Davide Pozza
>

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