On Tue, 1 Mar 2005 13:36:21 -0800, Brad Templeton <[EMAIL PROTECTED]> wrote: > > There are many types of suggestion engines I have been thinking about. > > Many people are familiar with Tivo's, which takes the log of what you > have recorded, or given thumbs up/down to, and remembers things like > titles, genres, actors and such. It then scans all new programming and > gives weights to how closely they match what it remembers and generates > suggestions from the best weights. > > One could code this for myth without any external data (though right now > myth I believe discards much of this information once a show has past, or > so I gather from looking at the tables that appear to contain it.) > > If you want to amalgamate data from other users, I am not sure how to > efficiently do that with a server running on your own machine. You could > have people just upload anonymized data to an open server which simply > gathers the raw data, and then the user's own machine sucks it down and > analyses it to generate suggestions. That seems to involve a lot of > data flow, or am I missing your goal. >
I saw this white paper written by a couple of Tivo engineers on how their collaborative recommendation system works. It's pretty interesting. Perhaps it could provide some benefit here. Original Word Doc: http://www.ics.uci.edu/~pazzani/Personalization/ali-kdd04.doc Google HTML cache: http://216.239.57.104/search?q=cache:EiwJD7vqU5gJ:www.ics.uci.edu/~pazzani/Personalization/ali-kdd04.doc%20%22determined%20by%20which%20channels%20are%20received%20at%20that%20TiVo%22&hl=en -Greg
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