Hi Gökhan, Also check out paper "Incremental Collaborative Filtering via Evolutionary Co-clustering"( http://www.dollar.biz.uiowa.edu/~street/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 > >> > >> > > >
