Predicting user preference for movies using netflix database

Online content and service providers deal with the problem of providing “relevant” content on a regular basis, especially due to the sheer vol- ume of data available. This work deals with one such problem, namely, that of predicting user preference for movies using the NetFlix database. We present a memory-based Collaborative Filtering (CF) algorithm that learns the personality traits of the users in a features space we call the La- tent Genre Space (LGS). This representation allows us to use traditional clustering algorithms in this space, and overcome one of the biggest prob- lems in these works – that of different lengths of user feature vectors in the vote space. Inference techniques in this space are discussed, and a kd-tree based nearest-neighbor scheme is implemented.

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