Just a thought, when you say to combine the metrics by multiplying their,
for example Sim1 = 0.9 and Sim2 = 0.2
When they are multiplied it would give a result of 0.18 which is very low,
remembering that they are pretty similar based on Sim1 - how can this
problem be tackled?
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Agata Filiana
If all of your similarities are a product like this, then they're all
low. In a relative sense this is fine.
But this is also why I proposed a geometric mean instead. For example
the geometric mean of these is about 0.424 and this notion can be
extended to include weights as well, which is what
I see it makes more sense with geometric mean. And with weight, if I want
to apply say 70% for Sim1 and 30% for Sim2, would it also make sense to
have it like this? The result should be around 0.194.
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Agata Filiana
Erasmus Mundus DMKM Student 2011-2013 http://www.em-dmkm.eu/
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On 17 April
Hi,
Continuing this discussion - I have the implementation, but I'd like to
know your opinion.
As I said before, I am creating a new implementation of UserSimilarity as
Sean pointed out.
Does it make sense to put weights into these metrics? Say I combined 3
similarity metrics: reading history,
In the usual recommender, the output is a weighted average of ratings.
In a model where there are no ratings, this has no meaning --
everything is 1 implicitly. So the output is something else, and
here it's a sum of similarities actually.
On Tue, Apr 16, 2013 at 3:05 PM, Agata Filiana
Well right now, I am only using one boolean file -just from from this
history of reading.
So you are saying the values generated in
the GenericBooleanPrefUserBasedRecommender is actually useless in this case
of no ratings and that it is merely based on the similarity only?
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Agata Filiana
Of course it's not meaningless. They provide a basis for ranking
items, so you can return top-K recommendations.
If it's normally based on similarity and ratings -- and you have no
ratings -- similarity is of course the only thing you can base the
result on.
On Tue, Apr 16, 2013 at 3:36 PM, Agata
Thanks a lot for the insight,very useful!
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Agata Filiana
Erasmus Mundus DMKM Student 2011-2013 http://www.em-dmkm.eu/
*
On 16 April 2013 16:40, Sean Owen sro...@gmail.com wrote:
Of course it's not meaningless. They provide a basis for ranking
items, so you can return top-K
I understand that, I guess what I am confused is the implementation of
merging the two similarity metrics in code. For example I apply
LogLikelihoodSimilarity for both item and hobby, and I have 2
UserSimilarity metrics. Then from there I am unsure of how to combine the
two.
On 18 March 2013
Ok, I will try that.
Thanks for the help Sean!
On 19 March 2013 12:02, Sean Owen sro...@gmail.com wrote:
Write a new implementation of UserSimilarity that internally calls 2 other
similarity metrics with the same arguments when asked for a similarity.
Return their product.
On Tue, Mar 19,
Hi,
Thank Sean for the response. I like the idea of multiplying the similarity
metric based on
user properties with the one based on CF data.
I understand that I have to create a seperate similarity metric - can I do
this with the help of Mahout or does this have to be done seperately, as in
I
You would have to make up the similarity metric separately since it depends
entirely on how you want to define it.
The part of the book you are talking about concerns rescoring, which is not
the same thing.
Combine the similarity metrics, I mean, not make two recommenders. Make a
metric that is
I understand how it works logically. However I am having problem
understanding about the implementation of it and how to get the final
outcome.
Say the user's attribute is Hobbies: hobby1,hobby2,hobby3
So I would make the similarity metric of the users and hobbies.
Then for the CF, using Mahout's
There is a difference between the recommender and the similarity metric it
uses. My suggestion was to either use your item data with the recommender
and hobby data with the similarity metric, or, use both in the similarity
metric by making a combined metric.
On Mon, Mar 18, 2013 at 9:44 AM,
In this case, would be correct if I somehow loop through the item data
and the hobby data and then combine the score for a pair of users?
I am having trouble in how to combine both similarity into one metric,
could you possibly point me out a clue?
Thank you
On 18 March 2013 14:54, Sean Owen
I'm not sure what you mean. The only thing I am suggesting to combine are
two similarity metrics, not data or recommendations.
You combine metrics by multiplying their values.
On Mon, Mar 18, 2013 at 12:54 PM, Agata Filiana a.filian...@gmail.comwrote:
In this case, would be correct if I
There are many ways to think about combining these two types of data.
If you can make some similarity metric based on age, gender and interests,
then you can use it as the similarity metric in
GenericBooleanPrefUserBasedRecommender. You would be using both data sets
in some way. Of course this
Hi,
I'm fairly new to Mahout. Right now I am experimenting Mahout by trying to
build a simple recommendation system. What I have is just a boolean data
set, with only the userID and itemID. I understand that for this case I
have to use GenericBooleanPrefUserBasedRecommender - which I have and
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