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 <[email protected]> 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 <[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. >>>> >>> >> >> > >
