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From Kevin Schiesser <kevin.schies...@xoom.com>
Subject Re: Implement LinkedIn's PYMK(People You May Know) feature use Mahout, any suggestions?
Date Thu, 25 Jul 2013 01:13:59 GMT
Hi,

I am also very new to Mahout.

My understanding is that Mahout aims to implement 10 machine learning
algorithms noted this paper
http://cs.stanford.edu/people/ang/papers/nips06-mapreducemulticore.pdf

Related to classification, try running the classify-20newsgroups example.
It's a good way to be sure the system is working, and to get familiar with
the input/output of Mahout.

-Kevin

On 7/24/13 09:12 , "Sebastian Schelter" <ssc@apache.org> wrote:

Jason,

You should also search the literature for "link prediction", thats the
academic term for the problem you describe.

This paper might be a good starting point:

"The Link Prediction Problem for Social Networks"

http://www.cs.cornell.edu/home/kleinber/link-pred.pdf?


2013/7/24 Ted Dunning <ted.dunning@gmail.com>

> I don't see the contact list of the potential connection.  Overlap of
> connection lists should be an extremely strong signal.
>
> You are correct that this tends to implemented be a classification
>problem.
>  The target variable is a binary variable that indicates whether the
>person
> knows or does not know the potential connection. Predictor variables
> include what you have described as well as many variants of the same.
>
>
>
> On Tue, Jul 23, 2013 at 9:28 PM, Jason Lee <wuaner@gmail.com> wrote:
>
> > Hi all,
> >
> > Currently i am working on recommendation system in a SNS site. There
>are
> > 15M+ registered members in our site. We already have a PYMK
> > implementation(not use mahout or any machine learning algorithms libs),
> but
> > the accuracy of recommend results produced by current implementation is
> not
> > as good as we expected, so i'm looking for a better way to implement
>this
> > feature.
> >
> > Here are some rules should be considered when recommend "People You May
> > Know" to current member: (any supplementaries?)
> > Contacts list imported by current member;
> > Same company:
> >     overlap of employed date range between current member and
>recommended
> > members;
> >     size of company;
> >     function of current member and recommended members;
> > Same login IP
> > Same school
> > Mutual Friends
> >
> >
> > As far as i know, Mahout is focus on CF(Collaborative filtering), but
> PYMK
> > is more likely a content-based recommendation, because the informations
> > that hold in member's profile is base of PYMK processing.
> >
>

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