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

Determining Song Similarity via Machine Learning Techniques and Tagging Information