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.
