Comparison of user and item-based collaborative filtering on sparse data

Sammanfattning: Recommender systems are used extensively today in many areas to help users and consumers with making decisions. Amazon recommends books based on what you have previously viewed and purchased, Netflix presents you with shows and movies you might enjoy based on your interactions with the platform and Facebook serves personalized ads to every user based on gathered browsing information. These systems are based on shared similarities and there are several ways to develop and model them. This study compares two methods, user and item-based filtering in k nearest neighbours systems.The methods are compared on how much they deviate from the true answer when predicting user ratings of movies based on sparse data. The study showed that none of the methods could be considered objectively better than the other and that the choice of system should be based on the data set.