A Music Recommendation System Based on logistic regression and eXtreme Gradient Boosting

Abstract

With the rapid growth of music industry data, it is difficult for people to find their favorite songs in the music library. Therefore, people urgently need an efficient music recommendation system to help them retrieve music. Traditional collaborative filtering algorithms are applied to the field of music recommendation. However, collaborative filtering does not handle data sparse problems very well when new items are introduced. To solve this problem, some people use the logistic regression method as a classifier to predict the user's music preferences to recommend songs. Logistic regression is a linear model that does not handle complex non-linear data features. In this paper, we propose a hybrid LX recommendation algorithm by integrating logistic regression and eXtreme Gradient Boosting(xgboost). A series of experiments are conducted on a real music dataset to evaluate the effectiveness of our proposed LX model. Our results show that the error and AUC of our LX model are better than other methods.