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Question: Describe how Netflix uses collaborative filtering software to match movie titles with customer tastes. List the ways this software helps Netflix garner a sustainable competitive advantage.
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Content-based filtering is one of the common methods in building recommendation systems. While I tried to do some research in understanding the detail, it is interesting to see that there are 2 approaches that claim to be “Content-based”. Below I will share my findings and hope it can save your time on researching if you are once confused by the definition.
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users or the films being identified except by numbers assigned for the contest. The competition was held by Netflix, an online DVD-rental and video streaming service, and.
In fact, as can be seen from the results page, a model-based system performed the best among all the algorithms we tried. References (1) J.S. Breese, D.Heckerman, and C.Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artifical Intelligence, 1998.
The term collaborative filtering refers to the observation that when you run this algorithm with a large set of users, what all of these users are effectively doing are sort of collaboratively--or collaborating to get better movie ratings for everyone because with every user rating some subset with the movies, every user is helping the algorithm a little bit to learn better features, and then.