Visualizing Netflix Rental Patterns January 10, 2010Posted by Andre Vellino in Recommender service, User Interface, Visualization.
The recent NY Times mashup of Netflix rental data with geographical data based on postal-codes illustrates just how informative such visualizations can be.
Take for instance the distribution of rentals in Washington DC of the movie Milk – based on the true story of Harvey Milk, the American gay activist who fought for gay rights and became California’s first openly gay elected official…
… and compare that with the distribution of rentals for The Proposal – a (straight) romantic comedy.
I think you could be forgiven for concluding that residents in the downtown core of Washington DC are more socially liberal than in its residential suburbs (or, of course, that downtown residents prefer serious historical dramas to fictional comedies – or both).
Imagine if you could do the same thing with labeled Bayesian or LSA models that characterize classes or intersections of classes of Netflix users (e.g. class types that might be labeled something like “highly-educated-and-well-paid-government-employee” vs. “unemployed-manufacturing-blue-collar-worker”). That could form the basis of a nice explanation interface to a movie recommender system.