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 >> >> >
