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: [email protected] > To: [email protected] > > (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 <[email protected]>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: [email protected] > > To: [email protected] > > > > (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 <[email protected]> > > 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. > > > >
