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From Agata Filiana <a.filian...@gmail.com>
Subject Re: Boosting User-Based with the user's attributes
Date Tue, 19 Mar 2013 10:59:09 GMT
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 21:49, Sean Owen <srowen@gmail.com> wrote:

> 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.filiana87@gmail.com
> >wrote:
>
> > 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 <srowen@gmail.com> wrote:
> >
> > > 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, Agata Filiana <a.filiana87@gmail.com
> > > >wrote:
> > >
> > > > 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
> GenericBooleanPrefUserBasedRecommender
> > > with
> > > > the boolean data set (userID and itemID).
> > > >
> > > > Then somehow combine the two?
> > > >
> > > > But at the end, my goal is to recommend the items in the second data
> > set
> > > > (the itemID, not recommend the hobbies) - does this make sense? Or
> am I
> > > > confusing myself?
> > > >
> > > > Agata
> > > >
> > > >
> > > > On 18 March 2013 14:23, Sean Owen <srowen@gmail.com> wrote:
> > > >
> > > > > 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 the product of two other metrics. Normalize both of
> > > those
> > > > > metrics to the range [0,1].
> > > > >
> > > > > Sean
> > > > >
> > > > >
> > > > > On Mon, Mar 18, 2013 at 6:51 AM, Agata Filiana <
> > a.filiana87@gmail.com
> > > > > >wrote:
> > > > >
> > > > > > 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 have to implement my own similarity measure? It would be great
> if
> > > > there
> > > > > > is some clue on how I get this started.
> > > > > > Is this somehow similar to the subject of *Injecting
> > domain-specific
> > > > > > information* in the book Mahout in Action (with the example
of
> the
> > > > > > gender-based item similarity metric)?
> > > > > >
> > > > > > And also how can I multiply the two results - will this affect
> the
> > > > result
> > > > > > of the evaluation of the recommender system? Or it should be
> > > normalized
> > > > > in
> > > > > > a way?
> > > > > >
> > > > > > Thank you and sorry for the basic questions.
> > > > > >
> > > > > > Regards,
> > > > > >
> > > > > > Agata Filiana
> > > > > >
> > > > > >
> > > > > > On 16 March 2013 13:41, Sean Owen <srowen@gmail.com> wrote:
> > > > > >
> > > > > > > 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 means learning a whole different
> > > > similarity
> > > > > > > metric somehow. A variant on this is to make a similarity
> metric
> > > > based
> > > > > on
> > > > > > > user properties, and also use one based on CF data, and
> multiply
> > > them
> > > > > > > together to make a new combined similarity metric for this
> > > approach.
> > > > > This
> > > > > > > might work OK.
> > > > > > >
> > > > > > > It can also work to treat age and gender and other features
as
> > > > > > categorical
> > > > > > > features, and then model them as 'items' that the user
> interacts
> > > > with.
> > > > > > They
> > > > > > > would not have much of an effect here given how many items
> there
> > > are.
> > > > > In
> > > > > > > other models like ALS-WR you can weight these pseudo-items
much
> > > more
> > > > > > highly
> > > > > > > and get the desired effect to a degree.
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > On Fri, Mar 15, 2013 at 4:37 PM, Agata Filiana <
> > > > a.filiana87@gmail.com
> > > > > > > >wrote:
> > > > > > >
> > > > > > > > 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
> > > > > > > works
> > > > > > > > fine.
> > > > > > > >
> > > > > > > > Apart from the userID and itemID data, I also have
the user's
> > > > > > attributes
> > > > > > > > (their age, gender, list of interests). I would like
to
> combine
> > > > this
> > > > > > into
> > > > > > > > the recommendation system to increase the performance
of the
> > > > > > recommender.
> > > > > > > > Is this possible to do or am I trying something that
does not
> > > make
> > > > > > sense?
> > > > > > > >
> > > > > > > > It would be great if you can give me any inputs or
ideas for
> > > this.
> > > > > (Or
> > > > > > > any
> > > > > > > > good read based on this matter)
> > > > > > > >
> > > > > > > > Thank you!
> > > > > > > >
> > > > > > > > Regards,
> > > > > > > >
> > > > > > > > *Agata Filiana*
> > > > > > > > Erasmus Mundus Student
> > > > > > > >
> > > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > > > --
> > > > > > *Agata Filiana
> > > > > > *
> > > > > >
> > > > >
> > > >
> > > >
> > > >
> > > > --
> > > > *Agata Filiana
> > > > *
> > > >
> > >
> >
> >
> >
> > --
> > *Agata Filiana
> > *
> >
>



-- 
*Agata Filiana
*

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