Collaborative Filtering Based on the Entropy Measure

This paper introduces a new memory based approach to ratings based collaborative filtering. Unlike existing memory based collaborative filtering approaches, this approach exploits the predictable portions of even some complex relationships between users while selecting the mentors for an active user through the use of the novel notion of selective predictability, which can be measured using the Entropy measure. The proposed approach has been tested using the MovieLens dataset, and it is expected that this approach should work equally well for any given dataset. This flexibility would make it possible to make use of this approach in a wide variety of application domains including e-commerce where recommendations need to be provided to users based on the ratings provided implicitly or explicitly by different users to different items in the past. However the items should represent a relatively homogeneous group like movies, music albums, compact, disks, books, software, research articles etc.