mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: User based recommender
Date Fri, 05 Dec 2014 19:10:28 GMT
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.
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message