I think that's a fine project indeed. It sounds even a little ambitious for a GSoC project. Understanding, implementing, and parallelizing this approach is not trivial. If you want to propose it, sure, but scaling it back a little is probably OK too. As always it's best to propose a simple project that you can complete, document and test, rather than a big project you half-finish.
On Fri, Mar 19, 2010 at 1:34 PM, cristi prodan <prodan.crist...@gmail.com> wrote: > IDEA 2 - Additions to Taste Recommender > --------------------------------------- > As a second idea for this competition, was to add some capabilities to the > Taste framework. I have revised a couple of papers from the Netflix contest > winning teams, read chapters 1 thourgh 6 from [1] and looked into Taste's > code. My idea was to implement a parallel prediction blending support by > using linear regression or any other machine learning method - but so far I > didn't got to a point where I would have a clear solution of this. I'm > preparing my disertation paper on recommender systems and this was the first > idea I got when thinking about participating to GSoC. If you have any ideas > on this and want to share them, I would be very thankful.
