Filmrekommendationer med matrisfaktorisering

A recommender system is a tool for recommending personalized content for users based on previous behaviour. This thesis examines the impact of considering item and user bias in matrix factorization for implementing recommender systems. Previous work have shown that user bias have an impact on the predicting power of a recommender system. In this study two different implementations of matrix factorization using stochastic gradient descent are applied to the MovieLens 10M dataset to extract latent features, one of which takes movie and user bias into consideration. The algorithms performed similarly when looking at the prediction capabilities. When examining the features extracted from the two algorithms there was a strong correlation between extracted features and movie genres. We show that each feature form a distinct category of movies where each movie is represented as a combination of the categories. We also show how features can be used to recommend similar movies