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From Yash Patel <yashpatel1...@gmail.com>
Subject Re: User based recommender
Date Sat, 06 Dec 2014 23:55:05 GMT
i have something that shows the user locations,however is it possible to
implement this without using apache spark shell as i found it quite
confusing to use without no examples.

I have a windows environment and i am using java in eclipse luna to code
the recommender.
On Dec 6, 2014 9:09 PM, "Pat Ferrel" <pat@occamsmachete.com> wrote:

> You can often think of or re-phase a piece of data (a column in your
> interaction data) as an action, like “being at a location”. Then use
> cross-cooccurrence to calculate a cross-indicator. So the location can be
> used to recommend purchases.
>
> If you do this, the location should be something that can have
> cooccurrence, so instead of lat-lon some part of an address. Maybe
> country+postal-code would be good. Something unique that identifies a
> location where other users can be.
>
>
> On Dec 5, 2014, at 11:10 AM, Ted Dunning <ted.dunning@gmail.com> wrote:
>
> Cross recommendation can apply if you use the multiple kinds of columns to
> impute actions relative to characteristics.  That is, people at this
> location buy this item.  Then when you do the actual query, the query
> contains detailed history of the person, but also recent location history.
>
>
>
> On Thu, Dec 4, 2014 at 7:17 AM, Yash Patel <yashpatel1230@gmail.com>
> wrote:
>
> > Cross Recommendors dont seem applicable because this dataset doesn't
> > represent different actions by a user,it just contains transaction
> > history.(ie.customer id,item id,shipping location,sales amount of that
> > item,item category etc)
> >
> > Maybe location,sales per item(similarity might lead to knowledge of
> people
> > who share same purchasing patterns) etc.
> >
> >
> > On Wed, Dec 3, 2014 at 5:28 PM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
> >
> >> On Wed, Dec 3, 2014 at 6:22 AM, Yash Patel <yashpatel1230@gmail.com>
> >> wrote:
> >>
> >>> I have multiple different columns such as category,shipping
> > location,item
> >>> price,online user, etc.
> >>>
> >>> How can i use all these different columns and improve recommendation
> >>> quality(ie.calculate more precise similarity between users by use of
> >>> location,item price) ?
> >>>
> >>
> >> For some kinds of information, you can build cross recommenders off of
> > that
> >> other information.  That incorporates this other information in an
> >> item-based system.
> >>
> >> Simply hand coding a similarity usually doesn't work well.  The problem
> > is
> >> that you don't really know which factors really represent actionable and
> >> non-redundant user similarity.
> >>
> >
>
>

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