The task of determining item similarity is a crucial one in a recommender
system. This constitutes the base upon which the recommender system will work
to determine which items are more likely to be enjoyed by a user, resulting in
more user engagement. In this paper we tackle the problem of determining song
similarity based solely on song metadata (such as the performer, and song
title) and on tags contributed by users. We evaluate our approach under a
series of different machine learning algorithms. We conclude that tf-idf
achieves better results than Word2Vec to model the dataset to feature vectors.
We also conclude that k-NN models have better performance than SVMs and Linear
Regression for this problem.