2017/04/12 by Renato L. F. Cunha, Cunha, Renato L. F., Evandro Caldeira +3
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1704.03844
openalex publication_date 2017/04/12 · openalex created_date 2017/04/28 · openalex updated_date 2026/07/28
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.