I want to create a model that tries to predict a user's behavior based on the random walks of similar users. The problem is similar to Netflix's recommendation challenge. One of the popular solutions was to use singular value decomposition to find movies that user would most likely like to see.
My question is more like this: what genre of a movie would a user like to see on a particular day? You move to each state with a probability, and one state could be "watching no movie". Does it make sense or even is it even feasible to approach the problem as a Markov process? How has this been done before?
I took only one class on Markov chains in college. Thanks.