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From Lukáš Vlček <lukas.vl...@gmail.com>
Subject Mail thread detection [was Email and Collab. Filtering]
Date Wed, 24 Aug 2011 20:06:34 GMT
Hi,

I would love to hear more about how exactly you detect (or define) threads
for emails (for example for Lucene or Solr public mail lists).

As far as I can tell this is quite complex problem and based on my
experience with many search web tools for mail lists this is still not
solved. Speaking about thread based recommendations there can be missed
important information if the thread is not detected correctly.
If this has been already solved then please do not hesitate to point me to
any references.

Reagards,
Lukas

On Mon, Aug 22, 2011 at 4:48 PM, Grant Ingersoll <gsingers@apache.org>wrote:

> I'm working on an example (well, examples) of using Mahout with the ASF
> Public Data Set up on Amazon (
> http://aws.amazon.com/datasets/7791434387204566) and I wanted to show how
> to use the 3 "C's" (collab filtering, clustering, classification) with the
> data set.  Clustering and classification are pretty straight forward, but
> I'm wondering about the setup around collaborative filtering.
>
> The motivation for recommendations is pretty straightforward:  provide
> people recs on emails that they might find useful based on what other people
> have interacted with.  The tricky part is I am not totally sure on a valid
> setup of the problem.  My current thinking is that I build up the rec.
> matrix based on whether someone has interacted with (initiated/replied) a
> thread or not.  Thus, the columns are the thread ids and the rows are the
> users.  Each cell contains the count of the number of times user X has
> interacted with thread Y.  This feels to me like it is a stand in for that
> user's preference in that if they are replying multiple times, they have an
> interest in that topic.  I have no idea if this will be effective or not,
> but it seems like it could be interesting.  Does it sound reasonable?  I
> worry that even in a really large data set as above it will simply be too
> sparse.
>
> Is there a better way to think about this from a strict collaborative
> filtering context?  In other words, I know I could do content-based
> recommendations but that is not what I am after here.
>
> -Grant
>
> --------------------------------------------
> Grant Ingersoll
> http://www.lucidimagination.com
>
>

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