Thanks for the super speedy response! Going on from what you said I've been reading up on the different SVD based variants used throughout the Netflix competition and working on my proposal. I'm focussing on what you suggested with aiming purely on the SVD-based recommender with the possibility of also optimizing the SVD code.
I was wondering when it comes to the proposal what sort of background detail should I be going into? Should I be talking about the use of SVD within a recommender situation for example? Or given that the Mentors already know this should I be discussing purely what sort of SVD-based recommender implementation I'm planning? I guess a question beside the question is am I aiming the proposal to people who are familiar with Mahout and Machine Learning or to other people as well? Many thanks Richard Sean Owen wrote: > It'd be a matter of making a brand-new distributed recommender. It > need not have anything to do with SVDRecommender, which is a fine but > separate non-parallel implementation. > > Tacking on distributed slope-one is fairly easy, I think. Both > together, with testing, documentation, etc. are certainly big enough > for a GSoC project, probably a bit too large. > > I'd be pleased to see someone do a quite thorough job with an > SVD-based recommender, and perhaps along the way analyzing and > optimizing the SVD impl itself, and documenting and testing well and > so on. That's a nice project IMHO. > > On Thu, Apr 1, 2010 at 6:41 PM, Richard Simon Just > <i...@richardsimonjust.co.uk> wrote: > >> Just looking for some clarification. As a GSoC project would the SVD >> option mentioned below be a case of integrating the distributed SVD of >> MAHOUT-180 with the existing SVDRecommender? >> >> If so is there still a full GSoC project there? or would I need to >> combine it with say making the slope-one recommender fully distributed too? >> >> Many Thanks >> Richard >> >> > >
