mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Sean Owen <sro...@gmail.com>
Subject Re: Add on to itemsimilarity
Date Wed, 25 Jan 2012 18:48:03 GMT
I am not sure that fits in to an item-based recommender since this is data
that is not about your 'items'.

You might use it to influence a user similarity metric in a user-based
computation.

Or better, don't try to use this data yet and see where you get with the
simple implementation.

Sean

On Wed, Jan 25, 2012 at 6:40 PM, Saikat Kanjilal <sxk1969@hotmail.com>wrote:

>
> Understood Sean thanks for your help, one other question I am trying to
> figure out what algorithms I could use along with item similarity that
> would take in the user's reservation and resort stay data and tie that into
> creating additional recommendation data points (to be more specific
> additional training data if you will) that could be fed into the item
> similarity algorithm.
>
> > Date: Wed, 25 Jan 2012 17:36:49 +0000
> > Subject: Re: Add on to itemsimilarity
> > From: srowen@gmail.com
> > To: user@mahout.apache.org
> >
> > (moving to user@)
> >
> > I think I understand more about what you are doing. It doesn't quite make
> > sense to say you will train a recommender on the output of the
> recommender,
> > but I understand that you mean you have some information about what users
> > have visited what attractions or shows.
> >
> > This is classic recommendation. You put that in, and it can tell you what
> > other attractions, shows, etc. the user may like.
> >
> > So going back to the beginning, I'm not yet clear on why that isn't
> already
> > the answer for you, since you have built this. Explain again what else
> you
> > are trying to do to filter or process the result?
> >
> > On Wed, Jan 25, 2012 at 5:25 PM, Saikat Kanjilal <sxk1969@hotmail.com
> >wrote:
> >
> > >
> > > Putting back on the list, we want to recommend new items in the park,
> an
> > > item could be:1) attraction2) restaurant3) show4) Ride5) resort
> > > Our real data if you will is the recommendations that result from
> > > understanding their preferences in more detail based on their
> reservations
> > > and resort stays.  So I wonder if our real data is our training data
> that
> > > the recommender can use for training and calculate predicted data
> based on
> > > that.
> > >
> > > Date: Wed, 25 Jan 2012 17:20:02 +0000
> > > Subject: Re: Add on to itemsimilarity
> > > From: srowen@gmail.com
> > > To: sxk1969@hotmail.com
> > >
> > > (do you mind putting this back on the list? might be a good discussion
> for
> > > others)
> > > What are you recommending to the user -- theme parks, rides at a theme
> > > park?
> > > Yes, you would always be recommending 'unknown' things to the user. You
> > > already 'know' how much they like or dislike the things for which you
> have
> > > data, so recommendations aren't of use to you.
> > >
> > > Of course, you can use both real and predicted data in your system --
> it
> > > depends on what you are trying to accomplish. The recommender's role is
> > > creating the predicted data.
> > >
> > >
> > > On Wed, Jan 25, 2012 at 5:12 PM, Saikat Kanjilal <sxk1969@hotmail.com>
> > > wrote:
> > >
> > >
> > >
> > >
> > >
> > > Actually let me more clear, we are building a recommendations engine
> for a
> > > theme parks experience,  the user preferences is something we are
> storing
> > > based on the user's reservations and analytics, this is something
> that's
> > > stored before the user rates any items and may or may not have a direct
> > > relationship to the recommendations the user makes as they go around
> the
> > > park.  This is due to the fact that the user recommendations could be
> other
> > > rides or attractions that exist outside of the actual preferences.
>  Its not
> > > clear yet to me how to tie these preferences into the item similarity
> > > results.
> > >
> > >
>
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message