Predicting movie Ratings using KNN

Abstract: Many services provide recommendations for their users in order for them to easily find relevant information. Thus, the development of recommender systems is important for these services to constantly improve. With the integration of new technology, it is common to implement recommender systems with the use of machine learning algorithms. This report investigates a method for recommender systems based on the machine learning algorithm K-nearest neighbors, or KNN. Specifically, the algorithm was used to predict what users’ would rate movies before they had rated them. In addition, the method was compared with the use of a baseline method taking the mean value of all user ratings as predictions. The objective of this study was to analyze the usefulness of KNN. The conclusion was that the implementation of a movie recommender system based on KNN received better results than using a baseline method.