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From Yash Patel <yashpatel1...@gmail.com>
Subject Re: User based recommender
Date Mon, 08 Dec 2014 09:22:00 GMT
most columns have different values,when you say preprocess do you mean
using classifiers ?

my dataset is highly structured in nature so i dont understand how a
classifier will work.
 On Dec 8, 2014 2:20 AM, "Pat Ferrel" <pat@occamsmachete.com> wrote:

> If there is some “filter” column that flags one type of item or another
> then yes. Otherwise you’ll have to preprocess your data for input.
>
> On Dec 7, 2014, at 2:27 PM, Yash Patel <yashpatel1230@gmail.com> wrote:
>
> 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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