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From Yash Patel <yashpatel1...@gmail.com>
Subject Re: User based recommender
Date Sun, 07 Dec 2014 22:27:20 GMT
Will cross recommendation still work considering item similarity checks
multiple columns for items and my dataset has only one column for items;it
contains different item ids.




On Sun, Dec 7, 2014 at 5:26 PM, Pat Ferrel <pat@occamsmachete.com> wrote:

> To use cross-recommendations with multiple actions you may be able to get
> away with using the pre-packaged command line job “spark-itemsimilarity".
> At one point you said you were more interested in the Mahout Hadoop
> Mapreduce recommender, which cannot create these cross-recommendations.
>
> I don’t see any need to use the interactive Mahout or Spark shell. Calling
> Scala from Java is pretty complex so I’d recommend starting from the
> running driver so you have a base of Scala code to start from. Calling Java
> from Scala is dead simple, it’s done throughout Mahout code. This should
> help make Scala a little less daunting. I use IntelliJ and there should be
> no problem using Eclipse in the same manner.
>
>
> On Dec 6, 2014, at 3:55 PM, Yash Patel <yashpatel1230@gmail.com> wrote:
>
> 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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