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