Fully content-based IMDb movie recommendation engine with Pearson similarity

Abstract

With the advancement of technology and the updating of information, more people choose to watch movies on the Internet. In the world of brust data, users often encounter the difficulty of finding their favorite movies. Implementing the movie recommendation system of the media platform is one of the most effective ways to solve this problem. In order to avoid the imbalance of user data in the actual operation process, the content-based recommendation model is adopted in this research, aiming to find their similar variables in each movie, to calculate similarity index between each movie through the matrix and Pearson formula. The advantage of this method is that the potential interest of users for each movie can be discovered, and the probability of the movie being recommended will not be caused by the problem of the popularity of the movie.