MOVIE RECOMMENDATION USING CONTENT-BASED AND COLLABORATIVE FILTERING

Abstract

As the online entertainment industry and the e-commerce markets grows rapidly, so is the need for efficient recommendation engines and efficient algorithms, for the business of the companies so that large amount of revenue can be generated. The thesis proposes a hybrid collaborative and content based filtering algorithm so that the online entertainment market can be benefited, especially the online movie market, which gives the plus points of both, semantics and frequency based filtering along with a collaborative based approach which predicts the ratings of every movie. In the end, this thesis shows the results of the proposed hybrid algorithm, along with the other known filtering techniques and algorithms used to recommend movies.