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 an
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
You can use the Mahout text pipeline, which will give you weighted vectors
based on TFIDF for each article. There is an example of this in Mahout in
Action for clustering. Then run the RowSimilarityJob on them instead of
clustering. This will give you a strength of similarity for each article pa
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
a
Hi Pat
Thanks very much for your suggestions. I will try to develop a
"recommender" based on that and if somebody
is interested in it, then I could contribute it as another example.
Thanks
Michael
Am 29.08.13 18:02, schrieb Pat Ferrel:
You can use the Mahout text pipeline, which will give
Hi Pat,
Thanks for your comment, I found it quite helpful.
I'm also trying to build a content-based
recommender. One question though:
How can I use RunSimilarityJob for online data?
I mean, I have a dataset and the approach you describe
works pretty well to precompute the similarity
matrix.
H
Yes.
The batch training data should be updated as needed but for some length of time
the RowSimilarity Model will be valid and useful even with brand new queries
that are made from articles not in the model. Remember however that the only
items you will get in results are ones in the training d
Hi Pat,
Thanks so much for your detailed response.
At the moment we do not have any metadata
about the articles but just their title & body.
In addition, in the dataset we have tweets from the user which
will never be in the output of the recommender
(we never want to recommend a user to see a
The solution you mention doesn’t sound right. You would usually not need to
create a new ItemSimilarity class unless you have a new way to measure
similarity.
lets see if I have this right:
1) you want to recommend news
2) recs are based on a user’s tweets
3) you have little metadata about eith
As per your question, we have not built anything yet
so, we are dealing with that problem: How to let the tweets
drive the recommendation of the news to be viewed.
The original idea was to find item-item similarity between the
user tweets and the news in order to deal with the cold-start
proble
In the simple case I’m not sure a collaborative filtering recommender is going
to work here. The items change too quickly to gather significant preference
data. Articles are your items, what is their lifetime? To do CF you need
relatively long-lived items and enough user preference data about th
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