Hi Ted, Yes, In paper they have mentioned the point that "locally optimized co-clustering gives poor result in iterative learning", so they have used evolutionary co-clustering that gives better result. Thanks Vineet Yadav
On Wed, Feb 2, 2011 at 1:12 AM, Ted Dunning <[email protected]> wrote: > Co-clustering typically doesn't give really hot results (at least in my > reading and experience). > > On Tue, Feb 1, 2011 at 11:25 AM, vineet yadav > <[email protected]>wrote: > > > Hi Gökhan, > > Also check out paper "Incremental Collaborative Filtering via > Evolutionary > > Co-clustering"( > > http://www.dollar.biz.uiowa.edu/~street/research/recsys10_ecoc.pdf<http://www.dollar.biz.uiowa.edu/%7Estreet/research/recsys10_ecoc.pdf>), > In > > paper, author proposed a method to use new data in collaborative > > filtering > > model incrementally. Here co-clustering is used to cluster row and > > column(items and user) simultaneously. Also check master thesis > > "RECOMMENDING ARTICLES FOR AN ONLINE NEWSPAPER " ( > > http://www.ilk.uvt.nl/downloads/pub/papers/hait/kneepkens2009.pdf). > > Thanks > > Vineet Yadav > > > > On Tue, Feb 1, 2011 at 10:32 PM, Ted Dunning <[email protected]> > > wrote: > > > > > Sebastian, > > > > > > Have you read the Elkan paper? Are you interested in (partially) > content > > > based recommendation? > > > > > > On Tue, Feb 1, 2011 at 2:02 AM, Sebastian Schelter <[email protected]> > > wrote: > > > > > > > Hi Gökhan, > > > > > > > > I wanna point you to some papers I came across that deal with similar > > > > problems: > > > > > > > > "Google News Personalization: Scalable Online Collaborative > Filtering" > > ( > > > > http://www2007.org/papers/paper570.pdf ), this paper describes how > > > Google > > > > uses three algorithms (two of which cluster the users) to achieve > > online > > > > recommendation of news articles. > > > > > > > > "Feature-based recommendation system" ( > > > > http://glaros.dtc.umn.edu/gkhome/fetch/papers/fbrsCIKM05.pdf ), this > > > > approach didn't really convince me and I think the paper is lacking a > > lot > > > of > > > > details, but it might still be an interesting read. > > > > > > > > --sebastian > > > > > > > > On 01.02.2011 00:26, Gökhan Çapan wrote: > > > > > > > >> Hi, > > > >> > > > >> I've made a search, sorry in case this is a double post. > > > >> Also, this question may not be directly related to Mahout. > > > >> > > > >> Within a domain which is enitrely user generated and has a very big > > item > > > >> churn (lots of new items coming, while some others leaving the > > system), > > > >> what > > > >> do you recommend to produce accurate recommendations using Mahout > (Not > > > >> just > > > >> Taste)? > > > >> > > > >> I mean, as a concrete example, in the eBay domain, not Amazon's. > > > >> > > > >> Currently I am creating item clusters using LSH with MinHash (I am > not > > > >> sure > > > >> if it is in Mahout, I can contribute if it is not), and produce > > > >> recommendations using these item clusters (profiles). When a new > item > > > >> arrives, I find its nearest profile, and recommend the item where > its > > > >> belonging profile is recommended to. Do you find this approach good > > > >> enough? > > > >> > > > >> If you have a theoretical idea, could you please point me to some > > > related > > > >> papers? > > > >> > > > >> (As an MSc student, I can implement this as a Google Summer of Code > > > >> project, > > > >> with your mentoring.) > > > >> > > > >> Thanks in advance > > > >> > > > >> > > > > > > > > > >
