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" <p...@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.dunn...@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 <yashpatel1...@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.dunn...@gmail.com> > wrote: > > > >> On Wed, Dec 3, 2014 at 6:22 AM, Yash Patel <yashpatel1...@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. > >> > > > >
