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
