Hi Gokhan
Thanks very much for the keywords and hints about this topic.
Will do some more research and probably come back again at some later stage.
Thanks
Michael
Am 29.08.13 16:53, schrieb Gokhan Capan:
Hi Michael,
Those are collaborative filtering examples, which would recommend a news
article i, to a user u, based on:
- A weighted average of other users' ratings on i (where weight is the
similarity of two users' rating histories)
- A weighted average of u's ratings on other items (where weight is the
similarity of two items' rating histories, that is, the users rated the
item and how they rated it)
- A combination of the user and item vectors from user and item latent
factor matrices, which are obtained by decomposing the original rating
matrix.
If you are expecting the system recommend to a user only the news articles
those have similar content to the older news articles that the user had
shown a positive interest before, this is content-based filtering.
Also, the example you mentioned (recommending brand new articles)
introduces a challenge called cold-start problem, and content-based
filtering can generalize to cold-start articles, too.
A search in user-list for content-based filtering/recommendation can help
you (I am saying this because there were some great discussions on how to
achieve this with Mahout, for example, with custom similarity measures). if
you can't find anything satisfying, we can discuss that further.
Best,
Gokhan
On Thu, Aug 29, 2013 at 4:21 PM, Michael Wechner
<michael.wech...@wyona.com>wrote:
Hi
I am looking for a recommender example for news articles which is making
suggestions based on a user profile (independent of other users/readers) or
more specific on the reading history of a user.
Let's say a specific user likes to read articles about cycling and
international politics and the content management system is saving the URL
history of all the articles which have been read by this specific user.
When the editorial stuff is creating new articles/stories, then the system
should make recommendations to this user when she/he is getting back online
or also when a new story has been created, then the recommender should
check whether this new story would be good fit/match for this particular
user and the system should send a notification.
I guess developing such a recommender is possible with Mahout, but since I
am new Mahout, I would appreciate any pointers to examples which are
similar to the functionality described above.
I am currently looking at the examples shipped with Mahout
https://cwiki.apache.org/**confluence/display/MAHOUT/**
RecommendationExamples<https://cwiki.apache.org/confluence/display/MAHOUT/RecommendationExamples>
but if I understand correctly these are based on what other people liked
and not what the person itself only liked,
or do I misunderstand?
Thanks for your help
Michael
