Inferring User Expertise From Social Tagging In Music Recommender Systems For Streaming Services

Suppliers of music streaming services are showing an increasing interest for providing users with reliable personalized recommendations since their practically unlimited offerings make it difficult for users to find the music they like. In this work, we take advantage of social tags that users give to music through streaming platforms for improving recommendations. Most of the works in the literature use the tags in the context of content based methods for finding similarities between songs and artists, but we use them for characterizing users, instead of characterizing music, aiming at improving user-based collaborative filtering algorithms. The expertise level of users is inferred from the frequency analysis of their tags by using TF-IDF (Term Frequency-Inverse Document Frequency), which is an indicator of the quantity and relevance of the tags that users provide to items. User expertise has been studied in the context of recommender systems and other domains, but, as far as we know, it has not been studied in the context of music recommendations.