Hi Ted,
I looked through the paper a while ago. The approach seems to have great
potential, especially because of the ability to include side information
and to work with nominal and ordinal data. Unfortunately I have to admit
that a lot of the mathematical details overextend my understanding. I'd
be ready to assist anyone willing to build a recommender from that
approach but it's not a thing I could tackle on my own.
--sebastian
PS: The algorithm took 7 minutes to learn from the movielens 1M dataset,
not Netflix.
On 01.02.2011 18:02, Ted Dunning 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]
<mailto:[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
