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. >>> >> > >
