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. >
