@Ted: Thanks for your great response. Just one little question. With > cooccurrence analysis and is focused on sparsification of the cooccurrence matrix to produce an indicator matrix
you mean things like user-item or item.item methods? Cheers, Klaus On Sat, Jan 11, 2014 at 10:06 PM, Rafik NACCACHE <[email protected]>wrote: > Sure Ted. Thank you. > I really liked "mahout in action" by the way. That was my very first > reading on ML ! > Regards, > > Regards. > > > 2014/1/11 Ted Dunning <[email protected]> > >> >> >> >> On Sat, Jan 11, 2014 at 12:30 PM, Rafik NACCACHE < >> [email protected]> wrote: >> >>> Thank you Ted, >>> >>> Though I did not get all the points, I get it that streaming records >>> won't be worth the hassle as far as recommendations are concerned, >>> >>> Meanwhile, you rung a bell when you talked about elastic Search. I might >>> have an idea how to use that, but that would be content based, and I need >>> something collaborative for my use case... >>> >> >> Check out the links, especially my talk at buzzwords. You can combine >> multiple forms of behavioral evidence and content evidence in a single >> query. You can even add many forms of business logic into the same query >> such as geographic constraints. >> >> >>> >>> >>> >>> 2014/1/11 Ted Dunning <[email protected]> >>> >>>> >>>> ... >>>> Here are the links: >>>> >>>> [1] http://research.microsoft.com/apps/pubs/default.aspx?id=122779 >>>> [2] http://tdunning.blogspot.com/2012/02/bayesian-bandits.html >>>> [3] >>>> http://tdunning.blogspot.com/2012/10/references-for-on-line-algorithms.html >>>> [4] >>>> http://tdunning.blogspot.com/2013/04/learning-to-rank-in-very-bayesian-way.html >>>> >>>> >>>> >
