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 <[email protected]> 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 <[email protected]> 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" <[email protected]> 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 <[email protected]> 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 <[email protected]> > > 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 <[email protected]> > > wrote: > >> > >>> On Wed, Dec 3, 2014 at 6:22 AM, Yash Patel <[email protected]> > >>> 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. > >>> > >> > > > > > >
